WTW's data products organization exists to turn data into a source of competitive advantage — powering smarter decisions, differentiating our client offer, and transforming how the business operates. As a Principal Data Engineer, you will be a senior technical voice in that effort, shaping the architecture and engineering practices that make it possible.
This is not a role for someone who wants to execute a defined plan. You will be working across two fronts simultaneously: building the core platform capabilities — the virtualized data lake, Common Data Layer, and data contract framework — that give WTW a unified, AI-ready data foundation; and embedding directly within product domains to deliver the high-quality, reusable data products that drive real business value. You will set the technical bar, define the patterns others follow, and bring genuine engineering craft to a transformation that is still being designed.
This role sits at the intersection of platform engineering and domain product delivery — a rare combination that requires both the depth to make hard architectural calls and the pragmatism to ship things that work today while building for tomorrow.
Responsibilities
The Role:
Platform Architecture & Core Infrastructure
Help lead the design and build of WTW's virtualized data lake — a single logical view across domains, virtual by default, materialized where needed — on Databricks (global) and Microsoft Fabric (regional).
Architect and implement the Common Data Layer (CDL): resolved entities, canonical dimensions and facts, standard metrics, semantic views, and cross-domain joins that provide reusable intelligence across the business.
Design and enforce the data contract framework — defining how domains publish, version, and share data with quality, schema, and SLA commitments that travel with every dataset.
Define the reference architecture for ingestion, transformation, serving, and observability across the platform; establish the patterns, guardrails, and tooling standards that engineering teams across domains will follow.
Own foundational platform capabilities including governance, access security, data quality frameworks, lineage, and CI/CD practices across the data estate.
Domain Data Product Engineering
Embed within product domains — Servicing, Placement, Claims, R&A — to deliver high-quality, well-modeled data products that serve both human and machine consumers.
Apply modern transformation practices using DBT to build reliable, testable, and well-documented data models that conform to CDL standards and domain data contracts.
Design data products for multiple consumption patterns: SQL, APIs, NL interfaces, AI agents, events, and file-based exchange — building for the full interface layer from day one.
Collaborate closely with Data Product Managers and domain data owners to translate product requirements into robust engineering solutions, balancing speed of delivery with long-term quality.
AI-Augmented Engineering
Actively leverage AI tooling — code generation, automated testing, pipeline monitoring, anomaly detection, documentation — to accelerate delivery and raise engineering quality across the team.
Build data infrastructure that is AI-ready by design: managing data as an agentic foundation where AI and people operate from the same trusted business definitions, semantic context, and quality standards.
Contribute to the design and enablement of machine interfaces — MCP, APIs, AI agents, vectorization, and low-latency query layers — that allow AI systems to interact reliably with WTW's data estate.
Stay at the forefront of AI-augmented data engineering practice and bring opinionated, tested recommendations on where new tooling creates genuine leverage.
Technical Leadership
Set and steward technical direction across the data engineering function; your architectural decisions will shape how WTW builds and operates data products for years.
Define and maintain engineering standards: coding patterns, testing practices, observability norms, and data product quality criteria that hold across domains and teams.
Mentor and elevate engineers across the organization — through code review, pairing, and design critique — raising the overall capability of the function.
Operate as a technical partner to Data Product Managers, architects, and senior business stakeholders; communicate complex engineering trade-offs with clarity and confidence.
Always
Champion data quality, lineage, and observability as first-class engineering concerns — not afterthoughts.
Build with reuse in mind; every asset you create should be designed to contribute to the Common Data Layer and serve more than one consumer.
Bring a product mindset to engineering: the measure of good data infrastructure is whether it enables great products, not whether it is technically elegant.
Qualifications
What you’ll bring
Experience
Solid experience in data engineering, with demonstrable depth across platform architecture, data modeling, and production-grade pipeline delivery.
Proven experience building or significantly contributing to a modern data platform at scale — lakehouse, data mesh, or equivalent — serving a large and complex organization.
Track record of setting technical direction and influencing engineering practice beyond your immediate team; you have been the person others look to for the hard calls.
Experience delivering data products that serve diverse consumers — analytics, APIs, AI/ML systems — with different latency, quality, and access requirements.
Background in financial services, insurance, or broking is a plus but not required.
Technical Skills
Deep expertise in Databricks, including Unity Catalog, Delta Lake, Delta Sharing, and the full Databricks data engineering and ML stack.
Strong command of DBT for modular, testable, and well-documented data transformation; a clear point of view on semantic modeling and metric layer design.
Fluency in Python and SQL; comfort with Spark for large-scale data processing and transformation.
Experience with modern data ingestion patterns across structured, unstructured, CDC, API, and streaming sources (ADF, Kafka, Event Hubs, or equivalent).
Working knowledge of data contract standards and tooling (e.g., ODCS), and practical experience implementing quality, schema, and SLA commitments in production.
Familiarity with the machine interface layer: APIs (REST, GraphQL), AI agent frameworks, MCP, vectorization, and low-latency query patterns for AI consumption.
Understanding of foundational governance capabilities: access security (Entra ID, Unity Catalog), data lineage tooling, CI/CD for data (Github Actions, Terraform, DBT Cloud), and observability practices.
AI Fluency
AI fluency is a core requirement of this role — in two distinct dimensions. First, you will design and build data infrastructure that powers AI-driven products and agent workflows; you need to understand what AI systems require from data and how to deliver it reliably. Second, you are expected to use AI actively in your own engineering practice — for code generation, documentation, debugging, pipeline design, and technical research — treating it as a force multiplier, not a curiosity.
Practical experience integrating LLMs or AI agents with data platforms — whether through RAG pipelines, semantic layers, vector stores, or agentic data access patterns — is a strong advantage.
How You Work
You think in systems — you see how individual components connect, where coupling creates risk, and how today's decisions constrain tomorrow's options.
You hold a high bar for engineering quality — correctness, testability, observability, and documentation are non-negotiable, not nice-to-haves.
You are pragmatic under pressure; you know when to build the right thing and when to build the thing that ships, and you are honest about the difference.
You communicate technical complexity with clarity — to engineers, product managers, and senior stakeholders — without losing precision or oversimplifying trade-offs.
You reach for AI instinctively as part of how you work, and you actively share what you learn with the team around you.
You are energized by greenfield scope; you do your best work when you are writing the playbook, not following one.
What we offer
Enjoy a benefits package designed to help you thrive, both professionally and personally. You'll receive 25 days of annual leave plus an extra WTW day to relax and recharge. Our comprehensive health and wellbeing offering includes private healthcare, life insurance, group income protection, and regular health assessments, all giving you peace of mind. Secure your future with our defined contribution pension scheme, featuring matched contributions up to 10% from the company.
We support your growth and balance with hybrid working options, access to an employee assistance programme, and a fully paid volunteer day to make a difference in your community. On top of these, you can opt into a variety of additional perks including an electric vehicle car scheme, share scheme, cycle-to-work programme, dental and optical cover, critical illness protection, and much more. Start making the most of your career and wellbeing with a range of benefits tailored for you.
Equal Opportunity Employer
We’re committed to equal employment opportunity and provide application, interview and workplace adjustments and accommodations to all applicants. If you foresee any barriers, from the application process through to joining WTW, please email candidatehelpdesk@wtwco.com
