Senior Data Engineer

Role Overview

• Design and implement a modern, cloud‑native data platform on AWS, with Redshift and dbt at its core
• Build scalable, reliable data ingestion and transformation pipelines from operational, product, and commercial systems, with best gold standard data models and metrics that support commercial, customer, operational, and product analytics use cases.
• Deliver trusted datasets that power core reporting, dashboards, and self‑serve analytics across the business.
• Partner across Data, Product, Engineering, Commercial, Operational and Finance teams to ensure data models align with real business value drivers.
• Ensure data platforms are extensible to support more advanced use cases (e.g. streaming data, advanced analytics, future machine learning and AI workloads).
• Collaborate with the Head of Data & AI to support emerging MLOps and AI data pipeline needs over time.
• Work with the Head of Data and AI, to define and implement best practices for data quality, testing, monitoring, and documentation using modern frameworks (e.g. dbt tests, data contracts, SLAs).
• Build secure, compliant data access patterns appropriate for healthcare and regulated environments, including data minimisation, hashing, tokenisation, and role‑based access.
• Own operational excellence (reliable, performant, scalable, cost aware and observable) for data ingestion and transformation pipelines.
• Act as a senior technical contributor and role model within the data function.
• Support knowledge‑sharing and mentoring as the team expands.

Requirements

• Proven experience building data platforms and pipelines from the ground up in a modern cloud environment (AWS, Azure, GCP, Redshift, Databricks, Snowflake, dbt, etc.)
• Strong data engineering fundamentals, including data modelling, ETL/ELT design, performance optimisation, governance, and reliability.
• Experience scaling data systems as usage, data volume, and organisational demand grow.
• Experience working in healthcare or highly regulated environments is desirable.
• Hands on experience with AWS data services.
• Strong experience with cloud data warehouses, ideally Amazon Redshift and/or Snowflake, Databricks.
• Production experience using dbt for data transformations, testing, and documentation.
• Solid SQL expertise and proficiency in at least one general purpose programming language (e.g. Python).
• Experience implementing data quality frameworks, data contracts, data access frameworks, and SLAs.
• Understanding of data security, privacy, and compliance considerations.
• Exposure to, or strong interest in, ML/AI data pipelines (Sagemaker, Bedrock) and MLOps concepts.
• Comfortable operating in ambiguity and autonomous enough to make pragmatic decisions in a greenfield environment that align to data principles.
• Can focus on delivering value fast and value enablement.
• Strong stakeholder communication skills, with the ability to translate technical concepts into business impact.
• A mindset focused on building pragmatic, sustainable, well governed systems rather than ad-hoc solutions and workarounds.
• Is curious, comfortable with ownership, and brings a continuous learner and improvement mindset.

Tech Stack

• Amazon Redshift
• AWS
• Azure
• Cloud
• ETL
• Google Cloud Platform
• Python
• SQL

Benefits

• Health insurance
• Retirement plans
• Paid time off
• Flexible work arrangements
• Professional development opportunities