Sr. Data Engineer

Title:

Sr. Data Engineer

Department:
Information Technology

Reports to:

Sr. Manager, Data Engineering

Location:

Dearborn, MI

Job Classification:
Remote

FLSA Status:
Exempt

Job Band:
Professional

The primary responsibility of this position is to design, build, optimize, and support secure, reliable, and scalable enterprise data solutions. The Senior Data Engineer provides technical leadership for Azure Databricks and Lakehouse architecture, data pipeline design and orchestration, data modeling and performance optimization, cloud-native data platforms, data governance and quality, and AI/ML-ready data products. This role mentors engineers and collaborates across business, analytics, architecture, security, and application teams to deliver trusted data for reporting, advanced analytics, machine learning, and operational use.

At Carhartt, the values of hard work—dependability, honesty, and trust—are rooted in the legacy of our founder, Hamilton Carhartt. His commitment to serving hardworking people continues to inspire everything we do. Guided by his legacy and our mission—We serve and protect all hardworking people by building durable products— we remain dedicated to upholding these principles in every decision we make and every product we create.

• Design, develop, test, deploy, and support scalable batch and streaming data pipelines using Azure Databricks, Azure Data Factory, Python, PySpark, SQL, and related cloud-native services.
• Design and evolve Lakehouse architecture using Delta Lake and Azure Data Lake Storage, including reusable data products and medallion-oriented data layers where appropriate.
• Create and optimize conceptual, logical, dimensional, relational, and physical data models that are aligned with business meaning and analytical requirements.
• Optimize pipelines, SQL queries, clusters, storage layouts, and processing patterns for performance, reliability, scalability, and cost effectiveness.
• Implement orchestration, dependency management, scheduling, monitoring, logging, alerting, error handling, recovery, lineage, and observability for production data workloads.
• Apply data governance and quality practices, including metadata, cataloging, validation, reconciliation, security, privacy, retention, and access controls.
• Engineer trusted, well-documented, and reusable datasets and features that support reporting, advanced analytics, data science, and AI/ML use cases.
• Lead technical design discussions, establish engineering standards and reusable patterns, conduct code and design reviews, and document architecture decisions and tradeoffs.
• Mentor data engineers through pairing, technical guidance, code reviews, troubleshooting, coaching, and clear documentation.
• Partner with business stakeholders, analysts, data scientists, product owners, architects, security teams, application teams, and vendors throughout the solution life cycle.
• Apply software engineering practices including Git-based source control, automated testing, CI/CD, deployment automation, and infrastructure configuration.
• Provide advanced production support, root-cause analysis, corrective actions, and continuous improvement for complex data platform and pipeline incidents.

• Bachelor’s Degree in Computer Science, Information Systems, Engineering, Data Science, or a related technical field or equivalent related experience.
• Graduate degree preferred.

• Minimum of 8 years of professional experience in data engineering, data warehousing, business intelligence, cloud data platforms, or a related field.
• Demonstrated experience designing, implementing, and supporting enterprise data solutions using Azure Databricks and Lakehouse…