Lead Data Engineer

Job description

We are seeking a Lead Data Engineer to own end-to-end data modeling and pipeline delivery as a major enterprise data lake is re-architected. You will work hands-on with Databricks and dbt, partner closely with US-based stakeholders and collaborate with an India-based engineering team. You thrive with high autonomy, clear documentation and strong ownership.

Responsibilities

- Own dimensional data models from requirements through deployment using Kimball-aligned patterns

- Design, build and maintain pipelines in Databricks and dbt to support a modernised data lake

- Translate business requirements into scalable datasets, anticipating future reporting and analytics needs

- Lead end-to-end delivery including development, unit testing, UAT support and release coordination

- Produce clear technical documentation in Confluence and maintain decision records and data definitions

- Partner with US-based stakeholders to align outcomes, scope and delivery expectations

- Collaborate with an India-based engineering team to coordinate build, reviews and handovers

- Support basic dashboarding and validation to confirm data meets business expectations

Requirements

- Dimensional modeling capability including star schema, fact and dimension design and aggregate awareness

- Hands on experience with python programming

- Databricks experience for data engineering development and operational support dbt (data build tool) experience including modular modelling, testing and deployment practices

- SQL proficiency for analytics-grade transformations and performance-aware querying

- Traditional ETL and data warehousing foundation adapted to modern cloud data stacks

- End-to-end delivery ownership across requirements, build, testing, UAT and deployment

- Clear communication and strong documentation habits for non-technical stakeholder engagement

- Self-directed working style with comfort operating under light-touch leadership

Nice to have

- Experience re-architecting enterprise data lakes or data warehouses in multi-phase programs

- Familiarity with release planning across distributed teams and time zones

- Exposure to basic BI development or semantic layer design to support reporting consumption