Contract Databricks Developer Jobs in Michigan (NOW HIRING)

Data platform and pipeline engineering

• Design, build, and operate the core data infrastructure: data lake, warehouse, orchestration, observability, and governance, using declarative configuration and infrastructure as code (Terraform or equivalent) so the platform is reproducible and auditable

• Partner with platform and domain teams to design ingestion pipelines and implement declarative configuration for data sources across the stack

• Architect the transformation layer: dimensional models, aggregation strategies, and incremental materialization patterns that balance query performance against pipeline cost at scale

• Own streaming and near-real-time data flows for fraud signal propagation, transaction status events, and verification webhooks, with the reliability expectations those require

• Build for scale: partition strategies, clustering, late-arriving data handling, and backfill patterns that hold up when data volume doubles

Business outcome ownership

• Own the source-of-truth models for the metrics the business runs on: ARR, NRR, churn, transaction volume, fraud detection rates, customer health scores, and operational throughput

• Make the numbers defensible: when a business leader challenges a metric, you can walk them through exactly how it is calculated, what is excluded, and why

• Partner with Product, Finance, CS, and GTM to translate business questions into data models and help teams measure what actually matters 

Engineering craft and standards

• Write production-grade Python and SQL: modular, tested, version-controlled, and reviewable by someone who was not in the room when you wrote it

• Implement CI/CD pipelines for data systems: automated testing, schema change detection, data contract validation, deployment gates, and cost optimization and performance tuning as ongoing practice, not one-time projects

Experience

• 6+ years in data engineering with primary, end-to-end ownership of a production data platform, not a supporting role on a large team

• Direct experience designing and operating streaming or near-real-time pipelines (Kafka, Kinesis, Pub/Sub, Flink, or equivalent) at production scale, including debugging failures under load

• Hands-on production experience with cloud-based data platforms (Snowflake, BigQuery, Redshift, Databricks, or equivalent) and a production-grade orchestrator (Airflow, Dagster, Prefect, or equivalent)

Technical depth

• Expert SQL and distributed systems: window functions, recursive CTEs, query plan analysis, query concurrency management, and optimization strategies that go beyond adding an index

• Strong Python for data engineering: production-quality pipeline code with error handling, idempotency, retry logic, and test coverage; Go is a meaningful plus

• Dimensional modeling mastery: you understand the tradeoffs between normalized and denormalized designs, when SCDs are the right tool, and how incremental strategies affect downstream query semantics

• Event-driven architecture fundamentals: exactly-once semantics, consumer group management, backpressure handling, offset management, and the operational realities of keeping a streaming pipeline healthy

• Warehouse internals: clustering keys, materialized views, partition pruning, and cost optimization strategies that keep query costs from compounding as data volume grows

• Flexible vacation
• 12 company-paid holidays
• 10 paid sick days
• No work on your birthday
• Health, dental, and vision Insurance (including a $0 option)
• 401(k) with matching, and no waiting period
• Equity
• Life insurance
• Generous parental paid leave
• Wellness reimbursement of $300/year
• Remote worker reimbursement of $300/year
• Professional development reimbursement
• Competitive pay
• An award-winning culture