Engineering Manager, Enterprise Data Warehouse & Analytics

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MachinifyHealthcare payment integrity

Location: Remote - USFull-TimeManager

SalaryBase Salary: $220,000-$260,000

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Job Details

Experience: 10+ years in data warehousing, data engineering, or analytics engineering, including 3+ years as a hands-on engineering manager.
Required Skills: PythonSQLMicrosoft Power BIAirflowdbtDatabricks

Bring 10+ years of experience in data warehousing, data engineering, or analytics engineering.
Have 3+ years of experience as a hands-on engineering manager.
Demonstrate expertise in dimensional modeling, including grain, fact types, conformed dimensions, slowly changing dimensions, bridge tables, and the bus matrix.
Have built and owned an enterprise warehouse across multiple source systems in production.
Have expert SQL skills, including complex queries and performance tuning, and experience with dbt models, tests, macros, packages, and CI.
Have production experience with Databricks, Spark SQL, Delta Lake, Unity Catalog, and medallion architecture; deep Snowflake or BigQuery experience will be considered.
Have built governed metrics using Databricks metric views, dbt Semantic Layer/MetricFlow, Cube, or Power BI semantic models; Power BI/DAX is preferred.
Have designed and operated production ETL/ELT pipelines with incremental loads, snapshots or CDC, and Airflow or a similar orchestrator; use Python for pipeline work.
Have experience with data testing, reconciliation, observability, and CI/CD for data.
Work effectively with Product counterparts and business stakeholders, translating requirements into technical designs and explaining trade-offs plainly.
Healthcare claims or payment integrity data, operational KPI development, LLM-based self-service analytics, and leading teams across time zones are nice to have.

Design and evolve the conformed dimensional model and review new models for grain and conformance.
Design ETL/ELT pipelines from source systems through bronze, silver, and gold on Databricks, using incremental loads, data contracts, and Airflow orchestration.
Build governed metrics in Unity Catalog metric views and Power BI semantic models.
Turn Product and Operations KPI definitions into certified, source-reconciled models and metrics for dashboards and self-service.
Own reconciliation, data tests, freshness and anomaly monitoring, SLOs, and data CI/CD.
Lead the migration of dbt orchestration from dbt Cloud to Airflow while maintaining data freshness.
Partner with Product and business stakeholders to shape requirements and own technical architecture, sequencing, estimates, and trade-offs.
Manage, coach, and hire analytics engineers and BI analysts; run sprint execution and set standards for code review, testing, documentation, and AI-assisted development.

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