Context
The client is in an active transition phase: parts of the data landscape are still on-premise, while Microsoft Fabric is becoming the target platform for integration, lakehouse and warehouse modelling and downstream Power BI consumption. This creates a hybrid reality where stability and delivery matter as much as modernisation. The team is building new Fabric capabilities while maintaining continuity for existing reporting and operational needs.
Role
You will join our data engineering team as they modernise the data platform from an on-premise data warehouse to a cloud-native Microsoft Fabric environment.
As a
senior data engineer
, you will contribute hands-on
to building reliable, scalable data pipelines
and delivering well-structured
data products
that support reporting and analytics.
You will work in a
hybrid landscape
where on-premise and cloud sources must coexist during the transition, so you are expected to be comfortable operating across both worlds. You are not being hired to redesign the entire platform, but to deliver
high-quality engineering work
, challenge weak implementation patterns and help the team mature its Fabric way of working through pragmatic technical input and clear documentation.
Profile:
MUST HAVE:
API ingestion patterns (REST, pagination, auth)
Cost and capacity awareness in cloud analytics platforms
Data quality engineering (checks, reconciliation, monitoring)
Dataflows Gen2
DAX (performance-focused measures)
Dimensional modelling and star schemas
Documentation, standards and knowledge sharing
Fabric Data Factory pipelines
Fabric Data Warehouse (tables, views, performance)
Hybrid enterprise landscape (on-prem plus cloud)
Lakehouse on OneLake (Delta)
Medallion architecture (bronze to silver to curated)
Mentoring and raising team capability
Microsoft Fabric (end to end)
Owning delivery and proactively managing risks and dependencies
Performance optimisation across pipelines, lakehouse and warehouse
PySpark and Spark DataFrames
Python for data engineering
Scrum hygiene (clean tickets, predictable delivery)
Semantic layer design (dataset to model to report coherence)
Source connectivity (ODBC, SQL, Salesforce, notebooks)
SQL (T-SQL, ELT patterns, optimisation)
Translating requirements into deliverable technical solutions
NICE TO HAVE
Data Products Modelling
Enterprise SaaS as a data source (example: CRM platforms)
Power BI semantic models