Engineering Manager – Enterprise Data Warehouse, Analytics

This is a fully remote position, open to applicants in United States.

📋 Description

• Take ownership of and enhance the conformed dimensional data model along with team standards.

• Evaluate new models for consistency and conformance.

• Design ETL/ELT pipelines that transition from source systems through bronze, silver, and gold layers on Databricks.

• Develop and manage incremental loads, data contracts, and orchestration using Airflow.

• Create governed metrics within Unity Catalog metric views and Power BI semantic models.

• Provide certified operational KPI models and ensure metrics are reconciled with source systems.

• Oversee reconciliation processes, data testing, freshness monitoring, anomaly detection, SLOs, and CI/CD for data.

• Lead the transition of dbt orchestration from dbt Cloud to Airflow.

• Collaborate with Product and business stakeholders to gather requirements.

• Manage the technical architecture, sequencing, estimates, and trade-offs involved.

• Supervise, mentor, and recruit analytics engineers and BI analysts.

• Conduct sprint execution and set standards for code review, testing, documentation, and AI-assisted development.

⛳️ Requirements

• Over 10 years of experience in data warehousing, data engineering, or analytics engineering.

• More than 3 years in a hands-on engineering management role.

• Proficient in dimensional modeling, covering aspects such as grain, fact types, conformed dimensions, slowly changing dimensions, bridge tables, and bus matrix.

• Proven experience in building and managing an enterprise data warehouse across various source systems in a production environment.

• Advanced SQL and dbt skills, including complex queries, performance tuning, models, tests, macros, packages, and CI practices.

• Hands-on experience with production Databricks utilizing Spark SQL, Delta Lake, Unity Catalog, and medallion architecture.

• Familiarity with creating governed metrics and semantic layers using Databricks metric views, dbt Semantic Layer/MetricFlow, Cube, or Power BI semantic models.

• Experience in designing and executing production ETL/ELT pipelines with incremental loads, snapshots, CDC, and Airflow or similar orchestrators.

• Proficient in Python for pipeline development.

• Knowledge of data quality practices, including testing, reconciliation, observability, and CI/CD for data.

• Ability to collaborate with Product teams and business stakeholders, convert requirements into technical designs, and communicate trade-offs in clear, understandable terms.

🏝️ Benefits

• Remote work flexibility from anywhere in the US.

• Comprehensive Medical/Dental/Vision coverage for employees and their families.

• A flexible and trusting work environment.

• Unlimited Flexible Time Off (FTO).

• Equity opportunities.

• 401(k) plan with employer matching.

• Excellent healthcare benefits.

• Flexible time off policy.