Data Engineer; FTC

Position: Data Engineer (FTC)
Job ReferenceF
0511

Location London/Bristol

FunctionITStatus

Full Time Job Type Fixed Term Contract Closing Date
31 Jul 2026

More details

About LPPIOne of the key priorities at LPPI is ensuring that our people work every day in an empowered, supportive, diverse and inclusive culture. Practically, this means recognising and valuing the uniqueness of every individual in terms of background, experience, beliefs and circumstances. This starts with the recruitment process, where we partner with agencies who share our common goal and advertise on a variety of job boards to access the broadest spectrum of candidates.

And once successful applicants are on board, we trust our people to work in the way that works best for them which typically involves a mix of office and home working plus an engagement model to ensure frequent two-way feedback.

We are very proud of our culture at LPPI and the benefits it brings to both our people and the business. But don’t just take our word for it! Recently, we invited our entire workforce to describe in three words what is great about us and they said – our people, flexibility and collaboration.

If you would like to join a company that is inclusive and forward-thinking, please read on.

Role Purpose The purpose of this document is to solicit proposals from quali?ed vendors to supply a ‘Hands-on’ Data Engineer’s role that aligns with LPPI’s purpose by supporting the delivery of innovative, supportable, and sustainable solutions that help LPPI achieve its vision of being a leading provider of pensions and investments.

This role is an 8 month Fixed-Term Contract.

Key Responsibilities Commercial project experience in delivering modern data solution using Medallion Architecture and experienced inWork closely with business analysts, and stakeholders to understand data requirements.

Creation of technical specifications and architecture visions covering quality attributes, diagrams, associated risks, technical and system requirements.

Collaborate with wider Data Team and Business Analysts to align data models with business needs

Build and maintain robust ETL pipelines to extract data from diverse sources (e.g. APIs, databases, flat files).Use Azure Git Repo, Boards and CI/CD deployment pipelines for version control of ETL, SQL & Reports Transform and load raw data into structured formats using business rules, data cleansing and enrichment techniques into a unified schema.

Design and implement reporting data models (e.g. star/snowflake schemas) to support analytics

Tune ETL activities for efficiency and scalability through data indexing, partitioning, as well as parallel processing strategies to reduce latency and improve throughput

Implement data validation rules & cleansing routines to ensure accuracy and consistency.

Monitor data pipelines for anomalies, errors, and performance issues

Implement access controls and encryption where necessary

Document ETL processes, data flows, and transformation logic for transparency and maintainability

Design and build interactive, visually compelling Power BI reports and dashboards tailored to business needs.

DP-203:
Data Engineering on Microsoft Azure or an equivalent certification. Additional certification / courses in use of Gen AI will be beneficial

Requirements for the role Technical Stack (Must Have):

Medallion, Delta Lake and metadata driven Architecture using MS Fabric or Synapse Analytics ETL pipelines

Data Transformation using SQL Query Language and Python notebooks

Data storage and warehouse using SQL DB, delta lake(Gen2), Snowflake Dimensional modelling using Kimball methodology

Power BI Interactive Dashboard and Paginated Reports using DAX functions, Power Query.

A familiarity in Investment Management and/or Financial Services Commercial experience working with business stakeholders within a cross-functional matrix environment.

Experience in the delivery of projects utilising Agile methodologies.

Must be able to understand and apply new technologies to stay current with best practices and trends in application and data architecture for financial services.

Confidence in handling, interpreting, and presenting complex numerical data and technological…