Sr. Data Engineer

Carhartt is hiring a Senior Data Engineer to design, build, and support secure, reliable, scalable enterprise data solutions. The role focuses on modern Azure data platforms and Lakehouse architecture, while also placing emphasis on technical leadership, production excellence, and building reusable, AI-ready datasets and features. This remote position is based in Dearborn, MI with limited travel for special events.

What you will deliver

• Design, develop, test, deploy, and support scalable batch and streaming pipelines using Azure Databricks, Azure Data Factory, Python, PySpark, SQL, and cloud-native services.
• Design and evolve Lakehouse architecture using Delta Lake and Azure Data Lake Storage, including reusable data products and medallion-oriented layers where appropriate.
• Build data models across conceptual, logical, dimensional, relational, and physical levels aligned to business meaning and analytics needs.
• Optimize performance and cost by tuning pipelines, SQL queries, clusters, storage layouts, and processing patterns for reliability and scalability.
• Implement orchestration and operational controls such as dependency management, scheduling, monitoring, logging, alerting, error handling, recovery, lineage, and observability for production workloads.
• Apply data governance and quality practices, including metadata, cataloging, validation, reconciliation, security, privacy, retention, and access controls.
• Engineer trusted, well-documented datasets and features that support reporting, advanced analytics, data science, and AI/ML use cases.

Leadership and collaboration

• Lead technical design discussions, establish engineering standards and reusable patterns, and conduct code and design reviews with clear documentation of architecture decisions and tradeoffs.
• Mentor data engineers through pairing, technical guidance, troubleshooting, coaching, and documentation.
• Partner across the organization with business stakeholders, analysts, data scientists, product owners, architects, security teams, application teams, and vendors across the solution life cycle.

Engineering practices and production support

• Use software engineering practices including Git-based source control, automated testing, CI/CD, deployment automation, and infrastructure configuration.
• Provide advanced production support, including root-cause analysis, corrective actions, and continuous improvement for complex data platform and pipeline incidents.

Qualifications

• Bachelor’s degree in Computer Science, Information Systems, Engineering, Data Science, or a related technical field (or equivalent related experience).
• Graduate degree preferred.
• Minimum 8 years of professional experience in data engineering, data warehousing, business intelligence, cloud data platforms, or a related field.
• Demonstrated experience with Azure Databricks and Lakehouse architecture in enterprise data solutions.
• Advanced experience with data pipeline design and orchestration, including batch and streaming patterns, dependencies, scheduling, monitoring, and recovery.
• Advanced SQL skills including data modeling, query optimization, performance troubleshooting, and complex analytical transformations.
• Professional experience with Python and distributed data processing frameworks such as PySpark.
• Experience with Azure Databricks, Delta Lake, Azure Data Lake Storage (ADLS), Azure Data Factory (ADF), Unity Catalog, Azure DevOps, and related Microsoft Azure data platform services.
• Experience optimizing cloud workloads for performance, scalability, reliability, and cost effectiveness.
• Working knowledge of data governance, metadata, cataloging, lineage, data quality, security, privacy, retention, and access-control principles.
• Experience preparing trusted, governed, reusable, and well-documented data products for reporting, advanced analytics, data science, machine learning, generative AI, and future AI-driven business capabilities.
• Experience with Git-based development, code reviews, automated testing, CI/CD, and deployment automation.
• Experience integrating data from SAP, SAP Datasphere, APIs, files, events, product and master data platforms, and third-party enterprise systems.
• Demonstrated technical leadership through architecture and solution design, engineering standards and best practices, design reviews, mentoring, code reviews, reusable patterns, and technical guidance.
• Excellent verbal and written communication skills for technical and business audiences.
• Strong cross-functional collaboration skills with business and technical teams.
• Ability to isolate complex problems, evaluate options, make practical decisions, and provide advanced production support during critical incidents.
• Professional cloud or data engineering certification considered a plus.

Benefits

• Comprehensive suite of medical, dental, and vision insurance
• Short-term and long-term disability insurance
• Life insurance
• Employee assistance program
• 401(k) plan with employer matching contributions
• Paid time off and designated paid holidays

Work setup and conditions

• Normal office environment with extended periods of time sitting, standing, and typing on a computer
• May require willingness to work some weekends if necessary
• Remote role with no regular requirement to be on-site; travel is limited to special events
• Carhartt is a tobacco free workplace