Top Data Engineer Jobs in Bristol

The Data Engineer will build and scale data pipelines, ensure data quality, implement dbt best practices, and empower cross-functional teams with self-service data access.

Build large-scale data pipelines and infrastructure for frontier AI model training. Develop data-processing models such as classifiers, quality filters, and labeling systems; design deduplication, quality scoring, labeling, and augmentation strategies; and create reliable tooling for researchers to explore and train on massive datasets. The role also evaluates how data quality and composition affect model outcomes, with web crawler experience as a bonus.

Migrate SQL Server data products and analytical workloads to a modern cloud platform. Redesign legacy SQL, ETL processes, and business logic; build scalable pipelines and analytical models; validate data quality; collaborate with stakeholders; and improve platform reliability, performance, cost efficiency, and AI-assisted data engineering automation.

Design, build and maintain scalable, automated cloud data solutions and pipelines. Provide technical leadership to a small engineering squad, own complex backlog tasks, develop data frameworks and standards, and ensure code quality via reviews and pair programming using Databricks, Python, SQL and Spark.

Design and maintain reliable data solutions, lead a squad of engineers, develop scalable data pipelines, and ensure adherence to data standards.

Design, deploy, and maintain AWS and Databricks data infrastructure, data lakes, and scalable ETL pipelines. Build custom ingestion services and connectors using Airbyte, Go, and Python; optimize performance, reliability, monitoring, and data quality. Implement cloud data governance, security, access controls, encryption, and compliance practices while collaborating with platform engineers, analysts, and stakeholders.

Build, optimise and stabilise AWS-based data pipelines (S3, Glue, Redshift, Lake Formation). Resolve technical debt, support the Architect on target-state design, collaborate with client engineering teams to transfer knowledge, and deliver production-ready platforms in regulated, high-volume gaming environments.

Build and operate Python-based data ingestion, ETL, detection, and forensic artifact extraction pipelines for a mobile EDR platform. Productionize threat detection prototypes, optimize large-scale data storage and queries, deploy services on AWS, and establish monitoring, validation, fault tolerance, and data quality controls. Collaborate closely with security researchers to develop reliable tooling and scalable systems for identifying mobile spyware and other threats.

Design, build, and scale cloud-native data warehouses and robust ETL/ELT pipelines (primarily BigQuery). Optimize performance, implement partitioning and cost controls, ensure data governance, observability, and regulatory reporting support, and collaborate with analytics and engineering stakeholders.

Own and operate Databricks production data pipelines for a predictive CRM platform: monitoring, incident response, cost and performance optimization, migrating legacy ETL, onboarding customers, enforcing Delta Lake architecture and multi-tenant isolation, and applying CI/CD, security, and observability best practices to ensure reliable, scalable data delivery.

Triage and resolve Master Data Management (Party, Risk, Reference) issues in Stibo STEP, run targeted data-cleansing sprints, monitor data quality dashboards, support releases/UAT, and maintain documentation and playbooks to prevent recurring data issues.

As a Python and Kubernetes Software Engineer, you will develop data analytics solutions using open source tools, collaborating with a distributed team.

Build and maintain production data pipelines and features in Python; own project lifecycle from design to reliable production; set technical direction for the Insights platform; improve engineering standards (testing, observability, data quality); apply AI across products and pipelines; help establish semantic/AI-ready data layers.

Design, build, and maintain scalable data pipelines and platform services to support analytics, ML, and product teams. Implement infrastructure-as-code (Terraform) and Kubernetes-based deployments, develop data services/APIs, and ensure data quality, observability, security, and compliance. Partner with cross-functional teams, document systems, and continuously improve performance and reliability.

Design, build, test and document secure batch and real-time data pipelines, perform logical/physical database design and data modelling, expand platform capabilities, analyse and resolve data processing issues, and ensure alignment with data architecture for downstream consumers.

Build, test, and document secure batch and real-time data pipelines for a government client; design logical and physical databases; extend platform capabilities; ensure data models align with architecture; perform data analysis and root-cause investigations to improve processing and availability for downstream consumers.

Develop stored procedures or notebooks to build 230 additional data items into DCoE tables/views. Create an extensible, config-driven process to add fields with minimal re-engineering. Produce self-documenting, well-commented code and collaborate with the DCoE team on delivery; DCoE handles business analysis, design sign-offs, data modeling, and testing.

The Senior Data Engineer will enhance backend systems for a social media app by managing databases, optimizing recommendation algorithms, and ensuring accurate recommendations.

Design, build and optimise ETL/ELT pipelines and data foundations across enterprise systems. Profile, cleanse, map and reconcile data, implement quality frameworks, develop scalable data models on cloud platforms, and work with stakeholders to establish repeatable data architecture, governance and reporting for transformation programmes.

Design and deliver scalable Customer Data Platform solutions (Oracle Unity), build and operate data pipelines and Customer 360 models, integrate CDP with data lake/CRM/marketing systems, lead architecture and data modelling workshops, ensure data quality, governance, security, and support CDP operations, and partner with Oracle implementation teams.

Define and build quality frameworks for human data programs, partner with clients and engineering to design evaluation, recruitment and annotation strategies, implement automated checks and dashboards, investigate data integrity, and upskill teams to deliver high-quality human data at scale.

Lead data architecture and strategy across OLTP, OLAP, and AI systems. Drive AI-first foundations (LLM orchestration, RAG, real-time inference), ensure DataOps/CI-CD, mentor technical leads, run architectural reviews, partner with product and engineering leadership, and optimise platform security, observability and costs.

As a Senior Data Engineer, you will design and develop large-scale data processing software, manage technical components, and mentor team members, while ensuring code quality and operational readiness.

Design, build, and maintain scalable analytics models and automated reporting. Partner with Sales, Product, Marketing, and Finance to define KPIs, deliver ad hoc analysis, improve data quality and observability, and establish analytics engineering best practices to enable self-service analytics.

Lead QA and test strategy for an AWS-native big data platform. Define automated test frameworks, validate large-scale data pipelines (PySpark/EMR), integrate tests into GitLab CI/CD, manage risk and reporting, mentor QA teams, and ensure data integrity across cloud data lake workflows.