Data Engineer

About us

We are a leading consultancy with a purpose to make an enduring impact on health and healthcare. We work with leaders and frontline teams to improve health, transform healthcare, drive adoption of innovation and create value through investment.

Our consultancy serves the entire healthcare sector,from payors and providers of care,to life science companies, health tech and sector suppliers and health investors. We provide end‑to‑end services, from strategy through implementation, accelerated by data,digital and AI.

We shape opinion through evidence‑based thought leadership on key issues affecting health. With unmatched ability to access and use health data, our consultants are a driving force for delivering positive and meaningful change.

About the role

The Data Engineer sits within the Data Innovation team and works day‑to‑day on the CFlakehouse, our Databricks platform that holds the routine healthcare data behind our analytical and product work. The role is hands‑on and delivery-focused: writing SQL and Python, building and maintaining automated pipelines, and turning raw data into structured, production‑ready material that consultants and clients can rely on.

CF runs an apprentice model of development. You will learn actively from senior engineers, contribute to the team’s collective knowledge, and take on greater scope as you grow. The work spans data cleansing and validation for client engagements, building and maintaining pipelines, and contributing to CF’s technical products, including through hackathons.

This is a good fit for an engineer with a couple of years of experience who wants to build a broad technical foundation in a data‑rich healthcare consultancy, work close to real client problems, and see how technical work turns into client value.

Responsibilities

The requirements, responsibilities and duties of the role will include, but are not limited to:

Engineering and delivery

• Build andmaintainautomated data pipelines using
  
  PySparkand Spark, with appropriate monitoring
• Build data models and pipelines under the guidance of senior colleagues, producing production-ready code or client-ready material
• Develop data quality,validation and consistency checks, and carry out data cleansing
• Develop unit, functional and integration tests, and follow the team’s Git and Git Hub workflow
• Participate in agile ways of working: keep user stories and tasks up to date, and contribute to stand‑ups, retros and show and tells
• Flag early when work is deviating from plan, and helpidentifyand deliver mitigations

Domain,client sand collaboration

• Build an understanding of healthcare data and the CFlakehouse: what is collected, how it is structured, and how new sources are brought in
• Work with teams to bring analytical insights to client problems
• Communicate technical solutions to non‑technical colleagues, and give and receive feedback well

Learning and contribution

• Learn best‑practice development processes from senior colleagues, and seek out new tools and techniques to apply
• Use AI tools to improve code quality and efficiency
• Support business development and bid writing on technical detail, and contribute to product development through hackathons and thought leadership

Requirements

The requirements of the role include:

• Around 2 to 3 years of experience in a data engineering or comparable technical data role
• Working SQL, used day‑to‑day on the lakehouse
• Intermediate Python for data transformation and automation
• Experience building automated data pipelines using PySpark or Spark, with appropriate monitoring
• Ability to write unit, functional and integration tests
• Comfort working to a Git and Git Hub workflow
• Curiosity about healthcare data and how it relates to care delivery and policy
• A clear communicator who knows when to ask for support and works well in a team

Desirable, and not expected on day one:

• Databricks lakehouse experience: notebooks, Workflows and Jobs, Delta Lake
• Unity Catalog basics (we will train)
• Awareness of cloud basics such as storage and access control, and of medallion structure, bronze to silver to gold
• Exposure to dbt and basic dimensional modelling

Flexible working

We follow a hybrid working model that balances in…