What you'll do :

- Own end-to-end delivery of data engineering and analytics engagements : scoping, estimation, architecture, execution, and production readiness.

- Design data platform architectures on Azure and Microsoft Fabric - lakehouse/medallion patterns, ingestion and transformation pipelines, dimensional and semantic models.

- Act as the senior technical point of contact for clients : run discovery, translate ambiguous business problems into delivery plans, and hold credibility with the client's own technical stakeholders.

- Lead and elevate a delivery team - set technical standards, review code and architecture, mentor mid-level engineers, and make the build-vs-defer calls.

- Manage scope and prioritisation across competing demands, and communicate trade-offs clearly to both clients and internal stakeholders.

Must-haves (screen on these) :

- 5+ years in data engineering / analytics engineering, with demonstrable end-to-end ownership of at least one non-trivial data platform build.

- Strong hands-on Azure data stack : Azure Data Factory, Synapse and/or Microsoft Fabric, ADLS Gen2.

- Production-grade PySpark and advanced SQL - not just authored, but optimised and debugged in production.

- Data architecture and modelling depth : medallion/lakehouse design, dimensional modelling (star schema, SCDs), pipeline patterns (ELT/ETL, batch/streaming, CDC).

- Genuine client-facing experience : someone who has scoped, estimated, and defended delivery directly with a client, not only through a manager.

- Evidence of technical leadership : code review, mentoring, owning standards.

Nice-to-haves :

- Microsoft Fabric in production (vs. Synapse-only).

- Power BI / semantic-layer modelling (DAX, tabular models).

- Relevant Azure certifications (DP-203, DP-600, or architect-level).

- Consulting or services-firm background - comfort with multiple concurrent clients.