Data Engineer

GM • Warren, MI

1d • Hybrid

About The Position

This role is categorized as hybrid. This means the successful candidate is expected to report to Austin, TX or Warren, MI three times per week, at minimum [or other frequency dictated by the business if more than 3 days]. The Role Join a team of builders shaping enterprise-grade data products and platforms that power analytics, customer experiences, and operational insights at scale. The role You will design, build, and operate reliable batch and streaming data pipelines, partnering closely with product, platform, and governance teams to deliver high-quality, secure, and discoverable data. How you’ll work Product mindset: outcome-driven, iterative delivery, and clear metrics. Quality first: automated tests, reproducible pipelines, and continuous improvement. Security and compliance by design: least-privilege access, data masking, and auditability. Collaboration: partner across platforms, governance, and product teams; communicate clearly with technical and non-technical stakeholders. Tools you may use here Languages: Python, SQL Compute and pipelines: Apache Spark, orchestration/workflows (e.g., Databricks Workflows/Airflow), containerized jobs where needed Storage/metadata: Parquet; lakehouse tables (e.g., Delta/Iceberg); catalog/lineage tools DevOps: Git, CI/CD, secrets management, observability (logs/metrics/traces)

Requirements

• BS in Computer Science, Engineering, or equivalent experience.
• 5+ years building data pipelines at scale with a modern data stack.
• Strong proficiency in Python and SQL, plus performance tuning of both.
• Hands-on experience with distributed compute (e.g., Apache Spark) and lakehouse/warehouse paradigms.
• Data modeling for analytics (dimensional/medallion), data contracts, and schema management.
• CI/CD (Git-based workflows) and infrastructure-as-code (e.g., Terraform) in a cloud environment.
• Practical knowledge of data security, privacy, and access control concepts.

Nice To Haves

• Streaming pipelines with technologies such as Kafka (or similar), including stateful processing and backpressure management.
• Lakehouse technologies (e.g., Delta Lake/Iceberg/Hudi), file formats (Parquet/ORC), and table optimization (Z‑ordering, clustering).
• Data governance and cataloging (e.g., Atlan/Unity Catalog/Collibra/Immuta) and automated lineage.
• Data quality frameworks (e.g., Great Expectations) and SLAs/SLOs for data products.
• Experience with Databricks or equivalent cloud data platforms and workload orchestration.
• Domain experience with IoT/telematics, energy, or mobility data is a plus.

Responsibilities

• Architect and implement scalable ETL/ELT pipelines and services using modern data platforms and best practices.
• Build streaming and micro-batch data flows, including schema evolution, late/out‑of‑order events handling, and exactly‑once delivery semantics where feasible.
• Model data for analytics and ML using layered “bronze/silver/gold” patterns, with clear data contracts, SLAs, and lineage.
• Embed observability (logging, metrics, tracing), data quality checks, and cost/performance optimization into everything you ship.
• Automate testing and deployments with CI/CD
• Collaborate with domain SMEs and data product owners to define requirements, acceptance criteria, and success metrics.
• Operate what you build: participate in on‑call/incident response rotations and drive RCA and preventative engineering.

Benefits

• GM offers a variety of health and wellbeing benefit programs.
• Benefit options include medical, dental, vision, Health Savings Account, Flexible Spending Accounts, retirement savings plan, sickness and accident benefits, life insurance, paid vacation & holidays, tuition assistance programs, employee assistance program, GM vehicle discounts and more.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level