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Today

Operations Data Engineer

Apple Austin, TX

Austin, TX

Today

Full time Ai/ml Data Engineering Artificial Intelligence Data

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Job Description

An Operations Data Engineer at Apple will help design and build modern, scalable data infrastructure that supports analytics, machine learning, and AI-driven decision-making across Operations. The role spans end-to-end pipelines, data modeling, orchestration and observability, and production AIML workflows.

Responsibilities

Partner with business and analytics teams to understand data needs and translate requirements into robust, scalable engineering solutions that influence Operations decisions
Design and implement end-to-end data pipelines and architectures across ingestion, transformation, and delivery for batch and real-time streaming workloads
Develop and maintain high-quality data models, including dimensional, relational, and knowledge graph-based approaches, using modern transformation frameworks such as dbt
Architect and run data workflows using orchestration tools such as Apache Airflow, including monitoring, alerting, and SLA management
Implement data observability, lineage tracking, and validation frameworks to maintain data integrity and trust across the platform
Collaborate with Data Scientists, ML Engineers, Software Engineers, and Analysts to operationalize models and ensure infrastructure supports production AIML workflows
Work with infrastructure and platform teams to manage cloud-native data environments using Snowflake, Spark, and open table formats such as Delta Lake and Apache Iceberg, with emphasis on performance, cost efficiency, and scalability
Use AI-assisted development tooling (including GitHub and Claude) and LLM-powered agents to accelerate pipeline authoring, code review, documentation, and transformation logic generation from natural language specifications
Apply DataOps practices including CI/CD, version control, automated testing, and containerization with Docker and Kubernetes to deliver reliable, production-grade data products
Adopt a data product mindset that enables self-serve analytics and reduces bottlenecks for downstream consumers
Tune query performance, partitioning strategies, and storage optimization for data at scale in cloud warehouses and lakehouses
Create and maintain technical documentation, including data dictionaries, lineage diagrams, and architecture decision records
Explain data infrastructure capabilities, health metrics, and architectural recommendations to senior leadership in clear, non-technical terms
Research and evaluate emerging data engineering technologies, including streaming architectures, GenAI-powered data tooling, and next-generation warehousing to improve team capabilities

Requirements

MS in Computer Science, Data Engineering, Statistics, Applied Math, Data Science, Operations Research, or related field and 8+ years of industry experience, or BS in a related field with 10+ years of hands-on industry experience
Domain expertise in supply chain, operations management, logistics, planning and forecasting, production integration, and channel management
Proven experience building and operating large-scale ETL/ELT pipelines with Python, SQL, and modern frameworks including dbt, Spark, and Kafka/Flink
Proficiency with cloud data platforms (such as Snowflake) and open table formats including Delta Lake and Apache Iceberg
Strong advanced SQL skills for complex data modeling, query optimization, and analytics engineering
Experience with workflow orchestration tools such as Apache Airflow (or equivalent) and delivering production-grade monitored pipelines
Hands-on implementation experience for data quality frameworks, observability tooling, and data lineage tracking in production environments
Experience implementing and productionalizing GenAI and Agentic AI tooling, including LLM-assisted code generation, MCP servers, and AI-powered data pipeline automation
Experience with data visualization and self-service analytics platforms (such as Tableau, Streamlit, ThoughtSpot) and ability to build lightweight front-end data products
Track record of adopting industry best practices, integrating emerging technologies (including vector databases, RAG pipelines, and AI-native data tools), and building functional prototypes to validate concepts

Technologies

Python, SQL, dbt, Spark, Kafka, Flink, Snowflake
Delta Lake, Apache Iceberg, Apache Airflow
GitHub, Claude, LLM-powered agents
Docker, Kubernetes
Tableau, Streamlit, ThoughtSpot
MCP servers, vector databases, RAG pipelines

Preferred Qualifications

Ability to work in a fast-paced, iterative environment and deliver projects under timeline pressure
Strong bias toward experimentation and continuous learning, with innovative thinking for reporting, business analytics, and AI-powered automation
Exceptional ability to communicate complex data architecture decisions to both technical peers and non-technical senior stakeholders
Strong interpersonal and collaboration skills for cross-functional partnership, knowledge sharing, and incorporating diverse feedback
Self-sufficient working style in environments with autonomy and ambiguity, with a bias for action and careful attention to data integrity

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1d ago

Senior AI Software Engineer

Kiewit Corporation Lone Tree, CO

Lone Tree, CO

1d ago

$130k - $150k/yr Full time Backend Developer APIs Application Security

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Job Description

Kiewit is seeking a Senior AI Software Engineer (hybrid) to build and run components of its enterprise AI platform. This role will own production features and services spanning AI workflows, backend systems, user-facing experiences, and Kubernetes-hosted workloads.

Key Responsibilities

Own features and services through production, clarifying requirements and acceptance criteria, designing and implementing changes across AI, backend, data, and UI components, and supporting releases after deployment.
Build and evaluate AI workflows by integrating language models, retrieval, and enterprise tools. Partner with domain experts to define task success and use representative evaluations to measure quality, failure behavior, latency, and cost.
Enable domain experts to deliver ideas by joining early in design, pairing on implementation, and guiding prototypes through architecture review, hardening, integration, and delivery while explaining tradeoffs and strengthening software practices.
Work directly with Kubernetes, deploying and troubleshooting containerized workloads and improving configuration, networking, storage, resource usage, observability, and rollout and recovery behavior. Coordinate with platform teams on shared infrastructure.
Review and improve implementation quality by examining human-written and AI-generated code for correctness, security, reliability, and maintainability, tracing changes across components, challenging assumptions, and addressing defect root causes.
Protect company data and user access by building integrations that respect user permissions, preserve data integrity, and handle AI-driven actions and failures safely in partnership with security and data owners.
Strengthen engineering practices through automated testing, code review, error handling, CI/CD improvements, documentation, and operational visibility, keeping solutions understandable and appropriately scoped.
Make capabilities clear and usable by carrying features through UI integration and documentation, maintaining visibility into ownership, dependencies, readiness, rollout status, and known limitations.

Required Qualifications

5+ years of professional software engineering experience designing, building, and maintaining production applications or services, including independent ownership of substantial features or services from design through deployment and ongoing support.
Bachelor’s degree in Computer Science, Software Engineering, Computer Engineering, or a related technical field, or equivalent practical software engineering experience.
Hands-on Kubernetes experience deploying, operating, and troubleshooting containerized applications.
Strong programming and debugging skills, with depth in Python or TypeScript and the ability to contribute across Python services and TypeScript/React web applications.
Ability to read unfamiliar code, identify consequential defects and architectural weaknesses, and clearly explain why proposed changes are sound.
Practical experience integrating LLMs or agent workflows into applications, including evaluating behavior and limitations.
Experience using AI-assisted development tools while independently reviewing, debugging, and validating generated code.
Strong foundations in API design, data modeling, distributed systems, authentication and authorization, testing, version control, and CI/CD.
Experience moving prototypes into maintained production systems with attention to security, failure handling, performance, and operability.
Clear communication and comfort supporting contributors with different technical backgrounds, including questioning assumptions and adjusting based on evidence.

Technologies

Python, TypeScript, React
Kubernetes, CI/CD
Language models, Retrieval, LLMs
API design, authentication and authorization
PostgreSQL
Azure, Azure Kubernetes Service (AKS)
Helm, FastAPI
Graph or vector retrieval, Model Context Protocol (MCP)

Preferred Qualifications

Experience with Azure and Azure Kubernetes Service (AKS), Helm, FastAPI, PostgreSQL, graph or vector retrieval, Model Context Protocol (MCP), and enterprise integrations would be especially relevant. Kiewit values depth in relevant areas and the ability to learn across the stack; familiarity with every listed technology is not required.

Benefits

Comprehensive benefits package including top-tier medical, dental and vision plans covering eligible employees and dependents
Voluntary wellness and employee assistance programs
Life insurance
Disability
Retirement plans with matching
Generous paid time off

What Success Looks Like

Independently delivering and supporting useful capabilities within ownership areas, demonstrating quality through software tests and AI evaluations, reducing recurring failures, and helping other contributors become more effective.
Building a product the team can confidently understand, operate, and extend, supported by reusable practices that enable additional use cases.

Role Details

Location: Lone Tree, CO (hybrid)
Compensation: USD 130,000 - 150,000 per year
Experience: Minimum 5 years

Other Requirements

Regular, reliable attendance
Work productively and meet deadlines timely
Communicate and interact effectively and professionally with supervisors, employees, and others in a team environment
Perform work safely and follow oral and written instructions, including warning signs, equipment use, and other policies
Work during normal operating hours to organize and complete work within deadlines; overtime and weekends as required
May work at various different locations, with conditions varying

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