Lead Data Engineer | Data Platform Architecture & Semantic Modeling | Global Insurance Group | London (Hybrid) | up to £135K base
About the Company
Our client is a fast-scaling, global commercial insurance group growing rapidly both organically and through international acquisitions across the UK, Europe, Australia, and Asia. Having acquired around forty separate businesses, they are rebuilding their technology operating model to turn a fragmented data landscape into a unified, highly governed platform.
Operating in an incredibly fast-paced, high-growth scale-up environment, they champion absolute autonomy and radical ownership. They move dynamically from start-up flexibility to scale-up discipline, meaning they listen well, move fast, and empower their people to execute without layers of bureaucracy or hand-holding. They are anti-bureaucracy but pro-governance—meaning regulatory compliance is delivered as code and automated platform controls rather than committees and paperwork. If a candidate is exceptionally bright, thrives in a rapid-iteration culture, and wants the freedom to define a roadmap and see their work directly move the business forward, they will find a massive opportunity here.
About the Role
Our client is completely re-engineering their tech division and hiring three bright, peer Lead Engineers to own and rebuild three brand-new pillars in the business. Reporting directly to the Group CTO with no layers of management in between, the successful candidate for this role will own the Data & Analytics pillar.
This is a hands-on, keyboard-level building role, not an administrative oversight position. You will lead a small internal team, which is set to grow, and partner with elite external data platform specialists to drive massive delivery leverage. The primary mission is to fix fragmentation. Across forty different acquired platforms, spreadsheets, and legacy SQL systems, you will build the unified platform and organisational habits that turn disparate records into one single version of the truth. You will also own the M&A data onboarding playbook, ensuring new source systems are ingested raw within a Day 30 window, and smoothly mapped to the shared model thereafter.
Key Responsibilities
- Manage and optimize the Snowflake data platform, ensuring strict compute/storage separation, warehouse sizing, and auto-suspend optimization.
- Build out a modern medallion architecture (Bronze, Silver, Gold), utilizing connector-led ELT tooling rather than bespoke hand-coded pipelines.
- Establish version-controlled, tested, and documented data models using dbt, ensuring data transformation logic is readable and auditable.
- Act as the technical and stakeholder bridge to establish canonical business definitions (e.g., agreeing on what a "client" or "claim" truly is) to drive unified analytics.
- Expose governed, highly reliable datasets through Power BI and Sigma, allowing business users to generate fast insights without risk of calculation error.
- Integrate Snowflake Cortex and LLM-based extraction to automate metadata mapping, write transformation code, and convert unstructured contract documents into clean, structured warehouse tables.
- Treat warehouse consumption as an engineering problem to instrument and control; build data residency and classification constraints directly into pipeline configurations.
- Deep, current, hands-on command of Snowflake (including in-platform AI capabilities like Cortex), dbt, modern ELT connector tools, and SQL.
- Solid grasp of medallion pattern design and the engineering discipline required to run ELT pipelines at scale.
- The organizational nerve and communication skill required to negotiate, establish, and close out standard data definitions across diverse business units.
- Knows when to buy vs. build, and strictly resists stretching operational integration platforms into serving as bulk analytical pipelines.
Qualifications & Experience
- Genuinely strong, hands-on engineering capabilities. They are hiring for trajectory and technical instinct rather than arbitrary years of tenure.
- Proven track record of designing, shipping, and running modern data architectures.
- Experience working within an FCA-regulated or highly structured environment, showing that data residency, security, and classification can be handled as design properties of the system.
- Desirable: Experience ingesting and harmonizing disparate acquired data estates, or experience with metadata catalogs (e.g., Atlan, Alation).
- Note: Certifications (SnowPro, dbt, or Azure Data credentials) are treated as evidence of depth, never as a barrier to apply.