Warren, MI

Engineer Jobs in Warren, MI

11 jobs found

Include remote jobs

Today

Machine Learning Engineer

Optimal Inc. Warren, MI

Warren, MI

Today

Full time Ai Ml Artificial Intelligence Data Science

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Job Description

Optimal Inc. is seeking a Machine Learning Research Scientist in Warren, MI to design and deploy AI models that merge physical principles with multimodal data. The role emphasizes Physics-Informed Neural Networks (PINNs) and predictive world models for simulating complex systems and forecasting real-world behavior.

Focus Areas

Physics-Informed Neural Networks (PINNs)
Predictive world models for learning system dynamics
Physics-informed machine learning approaches for simulation and forecasting

Responsibilities

Develop and implement Physics-Informed Neural Networks (PINNs) and other physics-informed machine learning models.
Build multimodal models that integrate inputs such as images, sensor measurements, time-series, and simulation outputs.
Create predictive world models to learn system dynamics, simulate environments, and forecast future states.
Integrate physics-based simulations with data-driven machine learning methods.
Validate accuracy, physical consistency, and performance using real-world or simulated data.

Required Qualifications

Master’s or PhD in Computer Science, Machine Learning, Applied Mathematics, Physics, Engineering, or a related field.
Hands-on experience with PINNs or scientific machine learning.
Experience with multimodal modeling and/or world model development.
Strong Python skills, with experience in PyTorch or TensorFlow.
Knowledge of neural networks, physical systems, numerical methods, or simulation.

Technical Skills

Python
PyTorch
TensorFlow

Preferred Qualifications

Research or industry experience in automotive, robotics, autonomous systems, or physical AI.
Experience with sensor fusion, differential equations, simulation, or predictive dynamics models.
Relevant research publications or project experience.

Location

Warren, MI (onsite)

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2d ago

Staff AI Engineer

General Motors Warren, MI

Warren, MI

2d ago

Full time Agentic Ai Ai Agent Ai Agent Platform

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Job Description

General Motors is seeking a Staff AI Engineer to design and deliver trustworthy AI systems and data products end to end. This onsite role in Warren, MI focuses on turning ambiguous business needs into measurable AI objectives, building production-ready capabilities, and operating them reliably across Sales, Service, Marketing, and Global Markets.

What you will do

Translate unclear business problems into defined AI objectives, solution strategies, evaluation plans, and measurable success criteria.
Develop, validate, and improve predictive and forecasting solutions, including classification, segmentation, optimization, natural-language, generative-AI, and decision-support approaches.
Select practical statistical, machine-learning, and AI methods aligned to the decision, available data, operational constraints, risk, and expected value.
Apply modern AI patterns when appropriate, including experimentation, time-series forecasting, causal analysis, retrieval-augmented generation, and agentic workflows.
Partner with stakeholders to determine when AI is the right approach versus simpler analytics, rules, or workflow automation.
Define data requirements and work with data engineering, platform, and business teams to establish reliable, documented data sources.
Build scalable data capabilities across ingestion, transformation, aggregation, features, embeddings, and knowledge pipelines using batch or streaming patterns.
Create reusable data products, analytical datasets, semantic models, feature assets, and retrieval indexes for multiple use cases.
Conduct exploratory analysis and data-quality work such as schema validation, feature selection, leakage detection, and data-availability analysis across structured and unstructured sources.
Work with customer, vehicle, dealer, sales, service, warranty, incentive, document, conversation, and operational datasets.
Deliver production-grade AI applications, services, APIs, assistants, dashboards, and workflow tools so AI capabilities are usable by end users.
Design interfaces connecting data pipelines, models, foundation models, tools, backend services, and front-end or workflow applications.
Implement retrieval, grounding, tool use, orchestration, prompt management, evaluation, and human-in-the-loop patterns where they fit.
Develop secure, testable, observable, and maintainable software with code review, version control, and automated testing.
Collaborate with architects and software engineers on structure, integration patterns, authentication, authorization, deployment, and operational support.
Establish evaluation frameworks tied to real-world business outcomes, then assess quality factors such as calibration, bias, interpretability, robustness, factuality, groundedness, safety, usability, latency, and operational fit.
Design and analyze experiments, pilots, red-team exercises, and champion/challenger validation to manage risk before broad adoption.
Deploy as APIs, batch processes, scheduled workflows, applications, assistants, or decision-support capabilities, with strong practices for versioning, lineage, prompt and model tracking, release management, rollback, and reproducibility.
Implement monitoring for data quality, drift, model and response quality, safety signals, application health, latency, availability, usage, and business outcomes.
Troubleshoot production issues, identify root causes, and improve models, pipelines, services, prompts, and user experiences through iterative refinement.
Apply responsible-AI, privacy, security, and data-governance principles, including controls for access, auditability, explainability, and human oversight.
Document assumptions, limitations, intended use, prohibited use, evaluation results, and operational ownership; coordinate with security, privacy, architecture, legal, and governance teams for compliant enterprise deployment.
Act as a technical authority across applied AI, machine learning, data engineering, application development, and AI platform patterns, raising quality through reusable patterns, reference implementations, documentation, testing, observability, and reproducibility.
Coach data scientists, analysts, engineers, and citizen builders on modeling, data, software development, and responsible-AI practices.
Support teams evaluating enterprise tooling such as Databricks, Azure AI, Azure AI Foundry, Glean, and other platforms when they improve delivery without sacrificing quality or governance.
Share lessons learned, reusable components, reference architectures, evaluation methods, and practical guidance across the AI Center and partner organizations.

What you bring

Bachelor’s degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related field (advanced degree preferred).
8+ years of professional experience across AI, machine learning, data science, data engineering, software engineering, application development, or a related discipline.
Proven experience moving AI, machine-learning, analytics, or data products from problem definition and proof of concept through production deployment and ongoing operations.
Strong Python and SQL skills, including production-quality code, testing, version control, documentation, and debugging.
Hands-on experience with tools such as Pandas, NumPy, scikit-learn, PyTorch, TensorFlow, or equivalent libraries.
Experience building or integrating AI applications, APIs, services, data pipelines, dashboards, and workflow tools.
Experience with model development, feature engineering, experiment design, statistical analysis, AI evaluation, and communicating results to nontechnical audiences.
Experience deploying and operating solutions via APIs, batch pipelines, notebooks-to-production workflows, applications, assistants, or similar production patterns.
Practical understanding of software and data engineering practices including CI/CD, automated testing, observability, dependency management, data quality, and release management.
Experience with large-scale data platforms such as Databricks, Spark/PySpark, cloud data warehouses, or equivalent technologies.
Ability to work independently, make sound technical tradeoffs, and deliver in a fast-changing cross-functional environment.

Technologies

Python, SQL, Pandas, NumPy, scikit-learn, PyTorch, TensorFlow
Databricks, Spark, PySpark
CI/CD, Azure AI, Azure AI Foundry, Glean
REST APIs, MLflow

Preferred qualifications

Master’s or PhD in Computer Science, Statistics, Machine Learning, Operations Research, Mathematics, Engineering, or a related quantitative field.
Experience in automotive, sales, service, marketing, customer analytics, dealer analytics, warranty, incentives, forecasting, or other operationally complex domains.
Experience building internal AI platforms, developer tools, data applications, decision-support products, assistants, or enterprise workflow solutions.
Experience with large language models including prompt engineering, retrieval-augmented generation, embeddings, vector search, tool calling, agentic workflows, or AI application evaluation.
Experience with causal inference, time-series forecasting, optimization, recommendation systems, natural-language processing, or generative-AI-enabled workflows.
Experience with MLflow or comparable tools for experiment tracking, model registry, evaluation, and lifecycle management.
Experience with Azure, Azure AI Foundry, Databricks, REST APIs, containerized deployment, CI/CD, cloud-native data services, and application observability.
Experience defining model governance, responsible-AI controls, data governance, security controls, interpretability practices, or risk-based evaluation standards.
Experience quantifying financial impact and partnering with Finance or business leaders to validate value realization.
Familiarity with enterprise AI and developer platforms such as Glean, Azure AI Foundry, Databricks, GitHub, or comparable platforms.

Immigration / sponsorship: GM does not provide immigration-related sponsorship for this role. Do not apply if you will need GM immigration sponsorship now or in the future.

Remote work / reporting requirement: This role is based remotely; however, if the selected candidate lives within a specific mile radius of a GM hub, they are expected to report to the location three times a week (or at another frequency directed by their manager).

Relocation: This job is not eligible for relocation benefits. Any relocation costs would be the responsibility of the selected candidate.

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10d ago

Staff Cybersecurity Software Engineer - Authorization Services

General Motors Warren, MI 🇺🇸 Remote

Warren, MI

10d ago

$160k - $246k/yr Full time Backend Developer Senior Application Security

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Job Description

Build secure authorization services at production scale with General Motors. This remote role in Warren, MI is for a staff-level individual contributor who will lead authorization capabilities, strengthen security engineering standards, and help teams deliver easy-to-operate security products. If you value reliability, clear technical guidance, and improving how services are designed and run in public cloud environments, this position offers room to set direction.

What you’ll do

Lead design and implementation of authorization frameworks that follow industry standards and expand beyond them.
Architect and build access control systems that use permissions, roles, and attributes to determine who can perform specific actions.
Define and implement policy-driven and distributed authorization approaches.
Drive security and authorization reviews across services and manage remediation of identified issues.
Design, develop, test, and maintain cloud-native microservices and APIs, setting technical direction for build, deployment, and operational practices on public cloud platforms.
Write clean, testable, and secure code using one or more languages such as Go, Java, Python, or JavaScript.
Author technical guidance, reusable patterns, and guardrails, then roll those practices out across product environments.
Define and implement observability including metrics, logs, traces, dashboards, and alerts.
Design data models and manage data storage using relational and graph databases.
Support critical production systems and participate in an on-call rotation.

Required qualifications

8+ years of hands-on application development experience, including authentication, authorization, or access control.
Bachelor’s degree in Computer Science, Software Engineering, Information Technology, or a related field, or equivalent expertise.
Deep expertise building and maintaining role-based and attribute-based access control systems for complex use cases.
Strong programming skills in one or more: Go, Springboot, Python, JavaScript.
Experience with infrastructure as code and deployment automation such as Helm, GitOps, Argo CD, or comparable tooling.
Experience applying AI-assisted software engineering across design, implementation, testing, debugging, documentation, and continuous improvement.
Advanced problem-solving with a focus on security, reliability, scalability, and maintainability.
Clear written and verbal communication, including leading design discussions.
Experience with modern cloud and container technologies such as Azure, Docker, and Kubernetes (K8s), plus infrastructure automation and comparable DevOps practices.
Experience building secure applications with identity, authentication, authorization, role-based access, secrets, and secure service-to-service communication.
Experience with Azure services such as managed databases, object storage, Key Vault, Redis, managed identity, or equivalent cloud services.
Experience with automated testing and CI/CD pipelines, including GitHub Actions or comparable technologies, source control, and production release practices.
Experience with observability: structured logging, metrics, tracing, health checks, alerting, and operational dashboards.

Preferred qualifications

Deep expertise in distributed application development, authorization frameworks, and policy engines.
Expertise modeling complex permissions and relationships in graph or relationship-focused data stores.
Demonstrated experience building highly available and scalable custom security frameworks and tooling.
Hands-on experience with IAM, OAuth, OIDC, SpiceDB, Zanzibar.

Compensation and incentives

The salary range for this role is $160,200-$246,300 per year. Bonus potential is available through an incentive pay program with payouts based on company performance, job level, and individual performance.

Remote and in-person expectations

This role is based remotely, but if the selected candidate lives within a specific mile radius of a GM hub, they will be expected to report to the location three times a week or at another frequency dictated by the manager.

Relocation, accommodations, and sponsorship

Relocation: Relocation benefits are available for candidates who qualify under company policy.
Immigration sponsorship: GM does not provide immigration-related sponsorship for this role. Do not apply if you will need GM immigration sponsorship now or in the future.
Accommodations: General Motors offers opportunities to all job seekers, including individuals with disabilities. For a reasonable accommodation during the job search or application process, email or call 1-800-865-7580.

Benefits

Health and wellbeing benefit programs (medical, dental, vision, Health Savings Account, Flexible Spending Accounts)
Retirement savings plan
Sickness and accident benefits
Life insurance
Paid vacation and holidays
Tuition assistance programs
Employee assistance program
GM vehicle discounts

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13d ago

Technical Software Engineer 2

Manpower Engineering Warren, MI

Warren, MI

13d ago

$7k - $140k/yr Full time Developer Engineering Software Development

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Job Description

Manpower Engineering is seeking a Technical Software Engineer 2 for onsite work in Warren, MI to support application development and systems implementation efforts.

Responsibilities

Design, develop, test, and maintain business and computer application software
Collect and analyze system requirements to shape technical software solutions
Review enhancement requests and implement software improvements
Develop and modify new and existing software programs
Perform unit testing for developed or updated software components
Conduct post-implementation validation, including troubleshooting and quality assurance reviews

Requirements

Bachelor’s degree in Computer Science, Computer Engineering, or a related technical field
2–4 years of software engineering or software development experience
Strong understanding of data structures, algorithms, and software design principles
Experience troubleshooting and testing, including support for both new and legacy systems
Proficiency in programming, problem diagnosis, and resolving software issues

Location and Work Setup

Warren, MI (onsite)
No travel requirement

Compensation

USD 6,711 - 139,590 per year

What’s in it for you

Work on sophisticated software implementation projects
Exposure to large-scale software systems and enterprise applications
Collaborate with experienced engineering professionals
Professional growth through hands-on development and problem-solving experience

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13d ago

Senior Cybersecurity Engineer - GRC Engineering & Technical Controls

General Motors Warren, MI

Warren, MI

13d ago

Full time Senior Cloud Platform Cloud Platforms

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Job Description

As a Senior Cybersecurity Engineer focused on GRC Engineering and Technical Controls, you will help General Motors advance enterprise cybersecurity risk management, regulatory compliance, and control assurance. The role designs, implements, and operates technical control frameworks that connect governance, risk, and compliance capabilities across cloud, connected vehicle, and operational technology environments.

Working in a hybrid setup from Warren, MI, you will lead initiatives to automate and scale evidence gathering, controls testing, and continuous assurance through integrated platforms, data pipelines, and policy-driven guardrails.

What you’ll do

Architect, build, and maintain data pipelines and middleware connecting GRC platforms with cloud environments, CMDBs, IAM tools, vulnerability scanners, and CI/CD pipelines.
Develop custom connectors, automation scripts, and API-integrated workflows to automate controls testing and evidence collection, reducing manual audit effort.
Standardize and codify security policies into machine-readable formats.
Implement guardrails using cloud-native policy engines and automated infrastructure-as-code checking tools.
Define and configure automated verification routines to monitor control effectiveness in near real-time and flag configuration drift or compliance gaps.
Develop and orchestrate AI-driven GRC workflows and agentic capabilities to automate risk assessments, control validation, and evidence classification.
Design and integrate AI agents and LLM-powered pipelines with GRC platforms to streamline policy parsing, generate compliance scorecards, and support remediation recommendations.
Interpret complex global, regional, and industry-specific regulations, including privacy and automotive standards, and translate them into engineering-focused technical control standards.
Maintain bidirectional traceability within a Unified Controls Framework (UCF) mapping regulatory obligations to engineering requirements (including NIST CSF, NIST SP 800-53, ISO 27001, SOC 2, and vehicle cybersecurity regulations such as ISO/SAE 21434 and UN R155/R156).
Conduct scheduled, event-driven, and automated technical audits to validate control implementations against regulatory and certification standards.
Serve as the technical administrator and authority for the enterprise GRC / Integrated Risk Management (IRM) platform.
Configure advanced workflows, relational schemas, and API access to support GRC operations.
Establish and aggregate automated compliance telemetry (for example, control coverage, deficiency aging, and real-time posture indicators) into executive-level dashboards.

Qualifications

Bachelor’s degree in Computer Science, Software Engineering, Cybersecurity, Information Systems, or a related technical discipline (or equivalent practical experience).
Minimum 7 years of progressive experience in a highly technical security environment, with strong focus on GRC engineering, security engineering, IT compliance, or DevSecOps.
Hands-on programming experience with Python, Go, PowerShell, or Bash for integrations, data parsing, and API interaction.
Hands-on experience or deep conceptual knowledge of leveraging AI agents, LLM APIs, and workflow orchestration frameworks to automate security and compliance tasks.
Strong understanding of Git-based workflows, CI/CD pipelines, and infrastructure governance tools, including familiarity with Policy-as-Code.
Practical knowledge of major security frameworks such as NIST SP 800-53, ISO 27001, or SOC 2, including translating regulatory or certification requirements into technical controls.
Practical knowledge of cloud infrastructure (AWS, Azure, or GCP), modern IAM models, vulnerability management architectures, and containerized deployments.
At least one active, industry-recognized professional certification in risk, audit, or security engineering (examples include CISSP, CRISC, CISA, or CGRC).

Technologies

Python, Go, PowerShell, Bash
AI agents, LLM APIs, workflow orchestration frameworks
Git, CI/CD pipelines, cloud-native policy engines
Infrastructure-as-code checking tools
AWS, Azure, GCP, IAM, containerized deployments
GRC, Integrated Risk Management (IRM), CMDBs, vulnerability scanners
Unified Controls Framework (UCF), NIST CSF, NIST SP 800-53, ISO 27001, SOC 2, ISO/SAE 21434, UN R155/R156

Benefits

Learn how GM supports a rewarding career that rewards you personally by visiting Total Rewards resources.

Preferred qualifications

Direct experience building machine-readable compliance, automated controls testing, policy engine integration, or continuous assurance programs.
Experience designing, configuring, and maintaining automated guardrails, automated evidence collection routines, and configuration drift detection in production environments.
Prior experience adapting unified control structures to map to complex automotive, vehicle safety, or international privacy standards.
Prior experience customizing and deep-configuring market-leading GRC/IRM platforms via custom APIs, database schemas, and workflows.

Professional certifications

Required: Holds at least one active, industry-recognized professional certification in risk, audit, or security engineering (examples include CISSP, CRISC, CISA, or CGRC).
Desired: Cloud or automation-focused technical certifications (examples include CCSP, AWS Certified Security – Specialty, Microsoft Certified: Azure Security Engineer Associate, or GIAC Cloud Security Automation).

Hybrid / relocation / immigration

This role is categorized as hybrid, requiring the selected candidate to report to a specific location at least 3 times a week or at a frequency set by the manager.
This role is not eligible for relocation benefits, and relocation costs would be the responsibility of the selected candidate.
GM does not provide immigration-related sponsorship for this role.

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14d ago

Senior Data Engineer - Vehicle Quality Engineering

General Motors Warren, MI

Warren, MI

14d ago

$125k - $169k/yr Full time Python Senior Azure

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Job Description

Drive production-grade data science and anomaly detection to improve vehicle, software, and product-quality outcomes.

Responsibilities

Partner with subject matter experts to define metrics, analytical goals, thresholds, and decision criteria
Perform unbiased analysis on vehicle, software, warranty, diagnostic, fleet, and product-quality data to find trends, anomalies, relationships, and emerging risks
Design, test, and deploy anomaly-detection and pre-emptive monitoring algorithms for customer-impacting issues
Build end-to-end analytical pipelines that convert raw data into reliable insights, dashboards, alerts, and operational workflows
Create alerting solutions that enable internal teams to respond quickly to high-priority vehicle and product issues
Establish validation approaches using historical data, controlled testing, domain expertise, and real-world observations when appropriate
Communicate findings, assumptions, limitations, and recommendations to technical and non-technical audiences
Improve model, query, pipeline, and architecture performance for scalability, reliability, and cost efficiency
Reduce complexity and redundant data handling by standardizing analytical processes and adopting modern Azure-based technologies
Support vehicle and software launches with readiness insights and analytical tools
Document analytical methods, data lineage, operating procedures, and known limitations to support maintenance and cross-team adoption
Deliver reusable patterns and best practices, including mentoring that increases broader analytics impact
Ship committed critical analytics features with clear acceptance criteria and production support plans
Launch or enhance pre-emptive monitors to identify emerging concerns earlier than reactive processes
Provide trusted dashboards and alerts for engineering, quality, warranty, and launch decision-making
Improve the performance, scalability, usability, and cost profile of existing analytics products
Demonstrate measurable customer value such as reduced manual effort, faster investigation, improved launch readiness, or earlier risk mitigation
Increase adoption by collaborating with software domains and operational teams on how insights are used and where capabilities should expand
Strengthen analytical governance through documented definitions, validation methods, data quality checks, and repeatable operating practices
Select analytical methods based on business questions, data limitations, and decision context; test assumptions using statistical and domain-informed practices
Balance detection sensitivity, false positives, explainability, and operational usability
Start with internal decision workflows and design solutions around those operational processes
Own outcomes across discovery, development, deployment, support, and continuous improvement
Troubleshoot issues quickly and follow assigned work through completion
Design maintainable, scalable, secure, and cost-conscious solutions
Explore practical new methods and technologies when existing approaches are insufficient; identify automation opportunities
Use validation and safeguards to minimize downside while taking educated risks
Collaborate effectively across engineering, quality, warranty, analytics, and business partners; listen actively and share information
Mentor others and contribute reusable knowledge
Develop pre-emptive detection for emerging product-quality issues and other customer-impacting patterns
Build algorithms to summarize suspect behavior and help teams identify problematic charging conditions or stations
Create dashboards and alerts for VIP vehicle concerns, launch readiness, fleet behavior, and production risks
Improve ingestion, query, and dashboard performance while reducing manual preparation and cloud-resource costs
Integrate additional signals including warranty, diagnostic, software, and operational data to improve detection confidence and business context

Requirements

Bachelor’s degree in Data Science, Statistics, Computer Science, Engineering, Mathematics, or related field, or equivalent practical experience
7+ years of data engineering/development or related experience
Experience applying statistical analysis and data-science methods such as machine learning, anomaly detection, forecasting, or classification to real-world business or engineering problems
Strong Python skills for data analysis, algorithm development, automation, and production-oriented data workflows
Experience using SQL and modern data technologies to query, transform, validate, and analyze large and complex datasets
Proven ability to take an analytical solution from problem definition through testing, deployment, monitoring, and continuous improvement
Ability to communicate analytical results and recommendations to stakeholders with varying technical depth
Strong data-quality mindset, including investigating discrepancies, assessing assumptions, and explaining limitations
Demonstrated ownership, sound judgment, responsiveness, and ability to deliver to completion in a cross-functional environment

Technologies

Python
SQL
Azure
Data Factory
Databricks
Git-based workflows
CI/CD
Databricks Asset Bundles

Benefits

Health and wellbeing benefit programs including medical, dental, vision
Health Savings Account
Flexible Spending Accounts
Retirement savings plan
Sickness and accident benefits
Life insurance
Paid vacation and holidays
Tuition assistance programs
Employee assistance program
GM vehicle discounts
Bonus Potential: incentive pay program with payouts based on company performance, job level, and individual performance

Location and Work Model

Warren, MI (hybrid)
Expected to report to Warren Global Technical Center or Austin Technical Center three times per week (or other frequency dictated by the business if more than 3 days)

Compensation

Expected base compensation: USD 125,000 - $168,650 per year
Bonus Potential: incentive pay program based on company performance, job level, and individual performance

Immigration

GM does not provide immigration-related sponsorship for this role
Do not apply if you will need GM immigration sponsorship now or in the future

Measures of Success

Accuracy, precision, recall, timeliness, and operational usefulness of deployed detection solutions
Adoption and satisfaction among engineering, quality, warranty, and launch-readiness customers
Reduced time to investigate issues, prepare meetings, extract data, or respond to high-priority risks
Reliability, performance, scalability, maintainability, and cost efficiency of production analytics products
Completion of committed critical features and effective production support
Quality of documentation, data definitions, validation practices, and knowledge sharing
GM behaviors: Own the Outcome, Think Customer, Innovate Now, Win with Integrity, Commit to Customers, One Team

Preferred Qualifications

Experience with Azure data services, Data Factory, Databricks, data pipelines, workflow orchestration, or comparable cloud technologies
Experience building operational dashboards, alerting tools, or decision-support products for engineering, quality, warranty, manufacturing, fleet, or customer-facing teams
Experience working with vehicle, telematics, diagnostic, warranty, software-version, or product-launch data
Experience with time-series data, event data, fleet-level analysis, VIN-level investigation, or software-quality monitoring
Experience with modern deployment practices such as Git-based workflows, CI/CD, or Databricks Asset Bundles
Familiarity with data modeling, data lineage, reusable analytical patterns, and scalable solution design
Experience evaluating whether AI/ML can improve detection quality while controlling false positives and operational burden
Experience mentoring peers, sharing technical best practices, or leading work across organizational boundaries

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1mo ago

Data Engineer

Community Choice Credit Union Warren, MI

Warren, MI

1mo ago Closed

$83k+/yr Full time Backend Developer Analytics Azure Data Factory

This position is no longer accepting applications (closed Oct 23, 2026).

Job Description

Build data foundations that improve reporting, enable real-time analytics, and support automation at Community Choice Credit Union. This on-site Data Engineer role in Warren, MI partners with IT, BI, and business teams to deliver reliable enterprise data pipelines, system integrations, and data warehousing solutions. Starting pay is $83,000 annually, with 3+ years of data engineering experience expected.

What you’ll do

Develop and maintain scalable ETL/ELT pipelines and system integrations that support enterprise data needs.
Design secure, API-driven integrations for internal systems and external partners.
Support data warehousing, analytics, reporting, and business intelligence requirements.
Implement both real-time and batch data integrations.
Monitor and troubleshoot data quality, reliability, and performance issues.
Establish data and API governance practices, including security, access control, versioning, and documentation.
Find opportunities to automate workflows and improve operational efficiency.
Collaborate across IT, BI, and business teams to deliver effective data solutions.
Document technical designs, integration patterns, and operating procedures.

What you bring

Three to five years of experience in data engineering, system integration, or automation.
Strong SQL, data modeling, and query optimization skills.
Hands-on experience building ETL/ELT solutions with Azure Data Factory.
Experience with Snowflake, Azure SQL, SQL Server, or similar cloud and on-premises data platforms.
Experience integrating REST APIs using authentication methods such as OAuth, API keys, and JWT.
Python experience related to API connectivity, automation, file processing, or data transformation.
Familiarity with Power BI, Power Automate, Azure Logic Apps, Git, Azure DevOps, and CI/CD practices.
Knowledge of data security, role-based access controls, and regulatory compliance.
Ability to manage multiple initiatives and collaborate effectively in an Agile environment.