Engineering Manager, Enterprise Data Warehouse & Analytics

Role overview

Lead a team building a trusted enterprise data warehouse and analytics platform across multiple business domains. The warehouse is already in production, with a lakehouse, extensive transformation models and tests, BI semantic models, and governed self-service analytics. The initial focus is operational measurement, followed by a repeatable approach to extending the warehouse. This is a hands-on management role, with roughly half the time spent designing models, coding, and reviewing engineering work.

Responsibilities

- Design and evolve conformed dimensional models, setting standards for grain and consistency across new models. - Build and operate Databricks ETL/ELT pipelines with incremental loads, data contracts, and orchestration. - Develop governed metrics and semantic models for dashboards, reports, and natural-language analytics. - Turn operational KPI definitions into certified metrics, reconcile them to source data, and deliver them through dashboards and self-service. - Maintain data quality through tests, reconciliation, freshness and anomaly monitoring, service objectives, and CI/CD. - Lead the migration of dbt orchestration to Airflow while preserving data freshness. - Set technical plans with Product and business partners, and manage, coach, and hire analytics engineers and BI analysts.

Requirements

- 10+ years in data warehousing, data engineering, or analytics engineering, including 3+ years as a hands-on engineering manager. - Deep dimensional modeling expertise, including fact types, conformed and slowly changing dimensions, bridge tables, and bus matrices; experience owning a multi-source enterprise warehouse in production. - Advanced SQL and dbt skills, including performance tuning, models, tests, macros, packages, and CI. - Production Databricks experience with Spark SQL, Delta Lake, Unity Catalog, and medallion architecture; deep Snowflake or BigQuery experience is also considered. - Experience building governed semantic layers and BI models; Power BI and DAX are preferred. - Background designing production pipelines with incremental loads, snapshots or CDC, an orchestrator such as Airflow, and Python. - Strength in data quality practices and in explaining technical decisions and trade-offs to business partners.

Nice to have

Healthcare claims or payment integrity data, operational KPI development, LLM-based self-service analytics, or experience leading teams across time zones.

Benefits and work setup

Remote work from anywhere in the United States. Benefits include medical, dental, and vision coverage for employees and families, flexible time off, equity, and a 401(k) with employer match. The listed base salary range is $220,000–$260,000.