We are looking for an experienced Principal Data Engineer to work hands-on on the re-engineering of an existing enterprise data platform built on Azure Synapse Analytics. The role requires strong technical depth to audit, understand, and validate a complex end-to-end data architecture spanning source ingestion through to consumption — and to help deliver the migration of validated workloads to Databricks. This is hands on role and demands the ability to reverse-engineer existing implementations, assess their correctness, and build to an agreed migration strategy.

Key Responsibilities
- Contribute to the technical assessment and re-engineering of an existing enterprise data platform, spanning all layers from source ingestion through to data consumption
- Reverse-engineer, document, and validate existing pipeline logic, data models, transformation frameworks, and data governance controls
- Identify gaps, defects, and technical debt across the platform and remediate where implementations are incorrect or sub-optimal

- Ensure correctness of data processing patterns including change data capture, slowly changing dimensions, deduplication, and business reconciliation
- Implement target-state designs aligned to modern lakehouse principles, ensuring feature parity and business logic fidelity during transitions
- Support platform evolution initiatives, including parallel-run phases where multiple implementations operate simultaneously, validating output consistency before cutover
- Execute migration of existing workloads to modern data platforms, preserving existing governance and control framework semantics
- Re-implement ingestion, transformation, and orchestration pipelines on target platforms, maintaining audit, quality, and reconciliation standards
- Collaborate with business, data governance, and architecture stakeholders to validate embedded business rules and data quality requirements
- Mentor less experienced engineers, review code and designs, and support decommis

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