Data Engineering Lead

Willis Re Inc; New York (NY); On-site; USD 160,000 - 180,000; Full time

Benefits offered by this job

Health insurance

401(k) savings and retirement programs

Paid time off

Employee assistance programs

Job summary

Willis Re Inc is building its global data estate with a Snowflake‑driven platform and seeks a Data Engineering Lead to drive implementation and governance. You will work hands‑on on ingestion, modelling and tagging, while defining data quality, lineage and cost‑aware architecture.

You will mentor engineers, partner with security and business stakeholders, and shape the data platform to scale for high‑volume reinsurance data.

Qualifications

• 10+ years in data engineering with end-to-end platform delivery.
• Hands-on Snowflake expertise across warehouse sizing, RBAC, ingestion, sharing.
• Strong governance: tagging, catalogues, lineage, access policies.
• Proven data quality, automated testing and monitoring.
• Deep data modelling, high-volume warehouse design and cost control.
• SQL & Python mastery; CI/CD and IaC on Azure.

Responsibilities

• Snowflake development: architecting the platform, ingestion and models.
• Establish tagging, cataloguing and governance for discoverable data.
• Own data governance, lineage, retention and cross-border residency.
• Define data quality controls, monitoring and reconciliation.
• Design scalable architecture with partitioning, archival strategy.
• Lead vendor work, enforce standards and review designs.
• Set CI/CD, observability and infrastructure-as-code practices.
• Mentor engineers and grow the data engineering team.

Skills

Snowflake

Data governance

Data quality

SQL

Python

CI/CD

Azure

Leadership

Vendor management

Terraform

Tools

dbt

Snowflake Cortex AI

Claude Code

Terraform

Azure DevOps

GitHub Actions

Job description

Overview

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.

About Willis Re: We combine specialist broking with analytics, modeling and research to help insurers optimize risk transfer, strengthen balance sheets and achieve sustainable growth. Our approach is relationship-driven, transparent and outcome-focused. At the heart of Willis Re is a focus on delivering the most cutting-edge analytical solutions to enable more informed, better decision-making for risk selection, portfolio optimization and capital management. The launch of Willis Re brings a strategic advantage of being unhindered by legacy, an ability to leverage data, statistical models and advanced technologies with the best knowledge and expertise to deliver more efficient and effective reinsurance outcomes. This places Willis Re in a unique position to build a truly analytically driven business, focused on creating solutions for the reinsurance industry that are future‑led and forward‑thinking. Willis Re will also leverage recognized technical expertise from WTW’s Insurance Consulting & Technology business including their advanced modelling and analytical capabilities. Alongside this will be WTW’s Research Network, an award‑winning business supporting and influencing science to improve the understanding and quantification of risk.

Key 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.

Qualifications

• 10+ years in data engineering, including experience leading the delivery of a significant data platform end‑to‑end.
• Deep, current, hands‑on Snowflake expertise across the platform and its ecosystem, including 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.
• Strong track record in data governance, covering tagging, 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.
• 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 skills; experience in pipeline orchestration, ELT tooling, CI/CD (Azure DevOps or GitHub Actions) and infrastructure‑as‑code (Terraform/Bicep) on Azure.
• 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, with the ability to challenge designs constructively and enforce standards without direct authority.
• 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.
• Strong leadership and communication skills: ability to lead engineers, influence stakeholders and explain technical trade‑offs clearly to both technical and business audiences.

Compensation & Benefits

• Base salary: $160,000 - $180,000
• Bonus: 20%
• Health and welfare benefits, paid time off, 401(k) savings and other retirement programs, and employee assistance programs.

Equal Opportunity & Accommodation

We provide equal opportunity to all qualified individuals regardless of race, colour, religion, age, gender, gender expression, national origin, veteran status, disability, orientation, or any other legally protected categories. If you have a need that requires accommodation, please email us at [e-mail].