Data Engineering Jobs

Palantir Technologies · Palo Alto, United States

New-grad engineers join Palantir's Foundations org, building shared infrastructure behind Foundry, Gotham and Apollo across backend, developer, frontend or storage tracks. Work spans the full product lifecycle, from design to rollout, with a dedicated mentor and close collaboration with technical and non-technical counterparts.

Palantir Technologies · Washington, United States · US$145,000 – US$155,000 pa

New-grad engineers join Palantir's Production Infrastructure org, contributing code to Kubernetes-based platforms like Apollo, Rubix and Signals, spanning hundreds of production clusters. You own the full product lifecycle with a dedicated mentor, working alongside product and deployment teams on mission-critical government and defense workloads.

Holt Executive · London

Hands-on full-stack role building data-driven applications, cloud platforms and AI-enabled tools. Day to day involves Python and TypeScript development, AWS services, Terraform, CI/CD and integrating LLM features, within a small collaborative team that contributes to technical design decisions.

Network IT · United Kingdom · £700 – £750 pd

Lead the design and implementation of data flows connecting operational systems, analytics and BI platforms, plus data streaming systems and database management. The role involves building and testing complex or large-scale data products, selecting technologies for resilient, scalable solutions, and designing metadata repositories. It also covers troubleshooting databases and data processes, and facilitating discussions across technical and non-technical stakeholders.

Forsyth Barnes · United Kingdom · £50,000 – £65,000 pa

Hands-on role designing, building and scaling an AWS-native data platform, migrating data from a large on-premise estate into the cloud. Involves building ingestion frameworks using CDC, event-driven and batch patterns, ETL/ELT with Python and SQL, plus data modelling, governance and observability. Also provides technical leadership and mentoring while remaining hands-on.

hireful · West Yorkshire · £80,000 – £90,000 pa

Owns the design and build of scalable data pipelines and data products on Databricks using PySpark, Spark SQL and Delta Lake, plus ETL/ELT optimisation and enterprise data architecture. Partners with Finance, Product, Sales, Marketing and Engineering to enable trusted self-service data, embeds governance and quality, and mentors a junior engineer in a small, high-impact team.

WeDoTech · London · £100,000 – £140,000 pa

Hands-on Staff-level role leading a large-scale data engineering modernisation, migrating thousands of pipelines across AWS. You'll stay deeply technical in Python, PySpark and Spark, shape scalable frameworks and standards, and champion AI-assisted development across multiple engineering teams.

Amazon · London

Design and build real-time middleware and runtime software connecting industrial control hardware to AWS cloud services, including data-exchange layers, type systems, and pub/sub or request/reply communication patterns. The role involves industrial protocol integrations (OPC-UA, Modbus, EtherCAT), edge-to-cloud telemetry with delivery guarantees, and engineering for bounded latency and real-time correctness on constrained hardware.

WeDoTech · London · £800 – £1,000 pd

A hands-on Staff-level role leading a major data estate modernisation for a UK financial services organisation, migrating large-scale pipelines and building new data products. You will shape architecture and engineering patterns while remaining deeply involved in Python, PySpark, Spark, AWS Glue and Airflow, and champion AI-assisted development across multiple teams.

Tenth Revolution Group · London · £80,000 – £90,000 pa

This role involves designing and scaling Azure Databricks-based data platforms, building robust data pipelines, and developing AI and LLM-powered capabilities. You will work closely with Product, Engineering, and Business stakeholders to deliver data products that support commercial decision-making and influence technical strategy.

Matillion · Manchester

Lead technical architecture and design for the Maia AI team, building agentic AI systems, tools and integrations while using evals to validate performance as models evolve. Mentor engineers, shape strategy with the Product Manager and Data Science team, and work with Java, AWS distributed systems and optionally React, TypeScript and Langchain/Langgraph.

DCV Technologies · London · £500 – £550 pd

Build and support data pipelines on GCP using BigQuery, Dataflow, Composer/Airflow, Cloud Run and DBT, with SQL and Python/PySpark for engineering and automation. The role sits in a Mobile Frameworks Lab within a major UK bank, focused on understanding customer interactions, and includes production support covering incidents, change requests and problem management.

Michael Page · United Kingdom · £60,000 – £65,000 pa

Designs, builds and maintains scalable Azure data pipelines using Data Factory and Databricks, with SQL, data modelling and Python or Scala. Works with analytics teams to gather requirements, optimise performance, troubleshoot workflows and implement data security and governance. Two 12-month fixed-term roles supporting a not-for-profit's analytics projects.

Harnham - Data and Analytics Recruitment · London · £70,000 – £85,000 pa

This role involves designing and maintaining data transformations, building scalable data products, and enabling reliable analytics through close collaboration with analysts and business stakeholders. The engineer will work extensively with SQL, cloud data platforms, and dbt to create trusted datasets that power business intelligence and decision-making. Emphasis is placed on best practices in analytics engineering, data modelling, and automation within a mature data environment.

SF Partners · Birmingham · £70,000 – £80,000 pa

Hands-on engineering role focused on developing and optimising Microsoft Azure and Microsoft Fabric platforms within a professional services organisation undergoing major transformation. Involves introducing automation, improving platform performance, resilience and cost optimisation, and establishing infrastructure and operational practices for growing data and analytics capabilities, while influencing technical standards and working with business stakeholders.