Senior Data Engineer
Employment Info
JOB DETAILS
Requirements
- 8+ years of experience designing and building production data solutions, with a track record of leading complex technical initiatives end-to-end.
- Track record of architectural ownership and designing end-to-end data solutions.
- Expert-level SQL and data transformation skills, with strong hands-on experience in dbt, PySpark, or both. You design data products for scale, write tests without friction, and have clear opinions on where each tool’s limits are.
- Deep experience with Databricks, Lakehouse architectures, or comparable modern data technologies, including Delta table design, Unity Catalog governance, compute trade-offs, and downstream BI or AI workloads.
- Experience implementing governance at scale, including access control, PII handling, column-level security, data lineage, and data quality management in production environments.
- Experience building trusted business-facing data products within Finance, People/HR, Procurement, Operations, or similar domains. You understand business logic well enough to challenge unclear or incorrect requirements.
Responsibilities
- Architect and own delivery of shared data products across Finance, People, Procurement, and Compliance, from design decisions through to production
- Build and evolve the semantic layer that powers reporting, self-service analytics, conversational analytics, and Databricks Genie, writing the models, tests, and documentation that make it trustworthy
- Design and build scalable data pipelines, data models, and governed data products using Databricks, dbt, SQL, and PySpark
- Implement governance capabilities end-to-end, including Unity Catalog access controls, column-level security, data classification, lineage, and data quality standards
- Drive DataHub adoption by defining metadata standards, lineage definitions, and data ownership models that make discoverability a first-class engineering concern
- Translate complex and ambiguous requirements from senior stakeholders into production-grade data solutions, owning the problem from conversation to deployed model
- Set the engineering standard for the team through the quality of your code, architecture decisions, and pull request reviews
- Partner with the broader data team to continuously improve CI/CD, testing frameworks, observability, and data quality practices
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