Data Engineering Lead

Willis Re•New York, NY

1d•$160,000 - $200,000•Remote

About The Position

Willis Re is building its global technology estate from the ground up, unencumbered by legacy and designed around data, analytics and modern cloud platforms. Our Snowflake data lake platform sits at the centre of that estate, and we are looking for a Data Engineering Lead to drive its implementation. This is a deeply hands-on role. You will spend most of your time building on Snowflake and establishing how data is tagged, catalogued, governed and quality-assured across the business, working alongside our architects and directing strategic delivery partners who build with you.

Requirements

10 + years in data engineering, including experience leading the delivery of a significant data platform end to end.
Deep, current, hands-on Snowflake expertise, having both built and architected across the platform and its surrounding ecosystem. This includes warehouse sizing, performance and cost optimisation, RBAC and access design, object tagging and masking policies, ingestion (Snowpipe, Streams and Tasks), sharing and marketplace, AI and ML capabilities such as Cortex, and transformation and orchestration tooling such as dbt.
A strong track record establishing data governance in practice, covering tagging and classification, data catalogues, lineage, stewardship and access policies.
Practical experience implementing data quality frameworks, automated testing and monitoring, and driving measurable improvement.
Deep expertise in data modelling and warehouse design, with sound judgement on schema design, performance and cost at high volume.
Proven experience running data platforms at high volume, including partitioning and clustering strategies, storage tiering, and defining retention and archival approaches that satisfy long-term regulatory obligations without runaway cost.
Strong SQL and Python, with practical experience of pipeline orchestration, ELT tooling, CI/CD (Azure DevOps or GitHub Actions) and infrastructure-as-code (Terraform/Bicep) on Azure.
Enough architectural depth to shape platform design and challenge it constructively, and experience building to security and compliance requirements in regulated financial services.
Experience leading or quality-assuring work delivered by vendors and partners, including the ability to challenge designs constructively and enforce standards without direct authority.
Ability to lead engineers, influence stakeholders and explain technical trade-offs clearly to both technical and business audiences.

Nice To Haves

Hands-on experience with Snowflake Cortex AI, or building AI and analytics use cases on data you have modelled yourself, is a significant advantage.
Familiarity with AI-assisted engineering tools such as Claude Code or Claude Cowork is a plus.

Responsibilities

Snowflake Development: Spend the majority of your time hands-on in Snowflake, architecting and building the platform and its surrounding ecosystem, including ingestion, transformation, data models, curated data products, and warehouse, performance and cost design for high-volume reinsurance placement, exposure, claims and market data.
Tagging & Cataloguing: Establish and maintain data tagging, classification and cataloguing, so that data across the platform is discoverable, well described and correctly labelled for sensitivity and business meaning.
Data Governance: Own governance for the platform, covering ownership and stewardship, lineage, access policies, retention obligations and cross-border data residency, working with security, risk and the business.
Data Quality: Define and implement data quality controls, automated testing, monitoring and reconciliation, and make quality visible and measurable to data consumers.
Scale & Archival: Design the platform to handle growing data volumes predictably, defining partitioning and clustering, storage tiering, retention and archival strategy, and keeping performance and cost under control as the estate grows.
Architecture Contribution: Contribute to the data architecture, working with the Solution Architect and Head of Architecture & Engineering to shape target-state designs and feed real-world constraints back into them.
Vendor Leadership: Direct and quality-assure the work of strategic delivery partners, reviewing their designs and code and holding them to the agreed standards.
Engineering Standards: Set how the team builds, covering CI/CD for data, automated testing, observability and infrastructure-as-code.
Grow the Team: Mentor engineers, raise the technical bar, and help shape how the data engineering function scales.