Senior Data Engineer - Databricks & Data Warehouse Lead

eNGINE; Pittsburgh (Allegheny County); On-site; USD 110,000 - 160,000; Full time

Job summary

eNGINE is seeking Senior Data Engineers to own and evolve our enterprise data warehouse on the Databricks platform, from raw ingestion to clean, trusted tables used by business analytics.

You will own data models, pipeline reliability, and quality checks, while helping build automation and event-driven architecture to move away from old batch patterns. Documentation is emphasized to ensure platform longevity.

Qualifications

• Hands-on data engineering with ownership of a warehouse or large pipelines with SLAs.
• Deep Databricks experience: Delta Lake, medallion architecture, Unity Catalog.
• Strong data modeling with conformance and harmonization.
• Advanced SQL plus PySpark and Python.
• Experience with Azure Data Factory, Data Lake Storage, and CI/CD.
• Ability to walk into a complex, loosely documented system and make sense of it.
• Excellent communication with technical and business stakeholders.

Responsibilities

• Own specific domains within the warehouse, including data models and pipeline reliability.
• Implement quality checks to ensure trustworthy data.
• Contribute to automation and event-driven architecture to modernize pipelines.
• Design and build pipelines across Bronze, Silver, and Gold layers.
• Write transformation and harmonization logic turning raw data into usable datasets.
• Develop Gold-layer tables for reporting and analytics.
• Maintain comprehensive documentation to prevent knowledge silos.

Skills

Data engineering

Databricks

SQL

PySpark

Python

Azure data stack

CI/CD

Data modeling

Stakeholder communication

Tools

Delta Lake

Unity Catalog

Azure Data Factory

Azure Data Lake Storage

Kafka

Job description

eNGINE is seeking Senior Data Engineers to own and evolve our enterprise data warehouse on the Databricks platform, from raw ingestion to clean, trusted tables used by business analytics.

You will own data models, pipeline reliability, and quality checks, while helping build automation and event-driven architecture to move away from old batch patterns. Documentation is emphasized to ensure platform longevity.