• Spearhead the discovery, evaluation, and integration of new datasets, collaborating (incl. pipeline development and data modeling/documentation) working closely with key data stakeholders to understand their impact and relevance to our core products and the healthcare domain • Facilitate the technical management of data assets - clearly tracking and maintaining context on the data within the dataset lifecycle and sustaining tight partnerships with immediate partners on ingestion & solution data engineering • Translate product / analytical vision into highly functional data pipelines supporting high quality & highly trusted data products (incl. designing data structures, building and scheduling data transformation pipelines, improving transparency etc.) • Set the standard for data engineering practices within the company, guiding the architectural approaches, data pipeline designs, and the integration of cutting-edge technologies to foster a culture of innovation and continuous improvement

• End-to-End Pipeline Engineering: Design, build, and deploy scalable ETL/ELT pipelines from diverse source systems into our Snowflake Data Cloud. • Cloud Infrastructure: Manage and optimize data flows within an AWS environment (S3, Lambda, IAM), ensuring high availability, security, and cost-efficiency. • High-Scale Processing: Leverage Databricks and Python (PySpark) to handle complex data transformations and high-volume workloads. • Implement the Semantic Layer: Collaborate with the team to define, implement, and scale our Semantic Layer (via dbt Semantic Layer, MetricFlow, or similar) to standardize business logic, metrics, and dimensions for all downstream consumers. • Model for Truth: Use dbt to build modular, version-controlled, and tested data models that serve as the definitive foundation for business intelligence.

• Define and own the enterprise data architecture strategy, including conceptual, logical, and physical data models across the organization's core domains • Establish and govern data standards, naming conventions, schema design principles, and modeling best practices used by Data Engineering and Analytics Engineering teams • Lead the design of scalable, reusable data products in the semantic and analytical layers, ensuring consistency across Decision Science, Data Science, and self-service consumption • Partner with the Product Data team to align on shared architectural standards, data contracts, and platform decisions—acting as a peer and collaborator, not a dependency • Evaluate and advise on data platform and tooling decisions (cloud data warehouses, lakehouse patterns, orchestration, metadata management, cataloging) • Identify and resolve architectural gaps, redundancy, and data quality risks across the data estate • Contribute to—and in many cases lead—the development of a business glossary, data catalog, and enterprise ontology for key data domains • Act as a senior advisor to Data Science on data availability, feature engineering infrastructure, and model data requirements • Collaborate with Decision Science leadership to ensure analytical data models are structured for performance, clarity, and governed self-service • Champion data governance, lineage, and observability as first-class architectural concerns

• Build and maintain BigQuery data models using Dataform, following medallion architecture patterns (Bronze/Silver/Gold) • Contribute to Looker dashboards and LookML models, working alongside senior engineers and analysts • Write performant, well-structured SQL for large-scale transformations in BigQuery • Implement data quality checks using Dataform assertions and automated alerting • Support data observability across the warehouse — monitoring pipeline health, data freshness, and anomaly detection • Build and maintain robust Python data pipelines with testing, linting, and CI/CD integration • Work with orchestration tooling (Cloud Composer / Airflow) to schedule and monitor workflows • Develop familiarity with CDC concepts and event-driven ingestion patterns (Datastream, Pub/Sub) • Containerise workloads with Docker for deployment on Cloud Run or similar GCP services • Support Data Scientists in moving work from notebook to production pipeline • Contribute to feature pipelines and data preparation for ML workloads • Help bridge the gap between research prototypes and scalable, maintainable code