Role: Lead Data Engineer – Azure/Databricks

Location: Sheffield

Working Model: Hybrid – 2 days per week in person

Salary: Up to £85k depending on experience

Own the data platform. Shape the future architecture.

This role is for a hands-on data engineering leader who wants to build something properly, not babysit legacy pipelines.

You’ll take full ownership of the data engineering domain, leading the design and building of a modern, highly scalable data platform that handles complex, high-frequency industrial data. You’ll work closely with data scientists, software engineers, and senior leadership, with the trust and mandate to make meaningful architectural decisions.

If you enjoy balancing strong engineering principles with real-world business needs, and you like mentoring others while staying deeply technical, this role is built for you.

What you’ll be working on

Architecting and rebuilding the data transformation layer in Databricks
Designing robust data flows that support both real-time operational views and deep historical analysis
Moving pipelines from ad-hoc scripts to software-engineering standards (CI/CD, testing, modular design)
Defining clear data models, schemas, and standards across a complex data estate
Establishing a stable, high-performance serving layer for analytics, visualisation, and data science workloads
Working closely with data scientists to remove bottlenecks and enable better modelling and experimentation
Pair-programming, mentoring, and raising the engineering bar for a small, capable team

You won’t just be delivering features; you’ll be setting direction.

The tech you’ll work with

Core: Azure, Databricks, Python, SQL, dbt, MQTT
Storage & Serving: Delta Lake, Postgres, TimescaleDB
Modelling & ML: MLflow
Visualisation: Grafana

What makes you a great fit

You’re someone who can zoom out to architecture and zoom in to code, comfortably.

Proven experience building and owning data platforms on Azure
Deep, hands-on knowledge of Databricks (lakehouse architecture, cluster management, performance and cost optimisation)
Strong opinions on data modelling, schema design, and standardisation
You treat data pipelines as software: version control, CI/CD, automated testing
Comfortable challenging architectural decisions, and explaining why
Able to translate technical trade-offs into business impact for non-technical stakeholders
A mentor by nature: you raise the team through pairing, guidance, and example

Industry experience

Experience with industrial, sensor-driven, or time-series data is highly desirable
Alternatively, background in high-volume or highly variable data environments (missing data, duplicates, schema drift, spiky load) will transfer well

Why you’ll want this role

Real autonomy: You’re trusted to make architectural calls, that’s why you’re being hired
Visible impact: Your work directly unlocks better analytics and machine learning outcomes
Modern problems: You’ll work on advanced data patterns and architectures, not cosmetic refactors
Still technical: This is a leadership role without stepping away from code

What you’ll get