Top Data Engineer Jobs in Bristol

Design and deploy production data pipelines on AWS from ingestion to consumption. Process large structured and unstructured datasets, integrate multiple sources, write scripts and SQL, and use Java/Python/Scala and Spark to build ETL, data lake and warehouse solutions while collaborating with cross-functional teams.

Lead design and delivery of GCP-based data solutions: build production data pipelines with Dataflow/Dataproc/BigQuery, design storage and architecture, implement CI/CD and data quality controls, mentor engineers, and drive standards, stakeholder alignment, and agile delivery.

Design, develop, and maintain ELT/ETL Snowflake data warehouse solutions; implement data standards, CI/CD, monitoring, and production support; collaborate with product, business, and vendors; mentor junior developers and improve data engineering practices.

Support ledger migration and data lake cleanup by extracting, transforming, reconciling and validating large datasets. Develop and validate SQL queries, batch jobs and processing logic, perform regression testing, document mappings and validation rules, and collaborate with engineering, product and business teams to ensure production readiness and post-migration validation.

Client-facing Principal Data Engineer leading design and delivery of enterprise Databricks-based data platforms. Provide architecture leadership, mentor engineering teams, drive platform optimisation, define standards, support technical governance, and deliver across multiple consultancy engagements.

Build the companys first data stack and data products (internal analytics and customer-facing). Work across data engineering, devops, and software tasks; ship early features, improve developer experience, document proposals, and iterate based on feedback.

Design, build, and maintain high-volume distributed data processing jobs and pipelines (Redshift, Athena, Iceberg, Spark, Airflow). Collaborate with data scientists and analysts to productionize models, optimize performance, troubleshoot systems, and mentor junior engineers. Support lakehouse/data warehouse management and ensure data accuracy and efficiency.

Design, build, and maintain scalable batch and streaming data pipelines, internal tools, and APIs to support analytics, product features, and ML. Collaborate with product and engineering to improve data infrastructure, ensure reliability, testing, and observability in a cloud-native environment.

Build and operate distributed ingestion and processing pipelines, ensure reliability and performance, define data contracts, add observability and testing, improve platform reliability and CI/CD, and participate in design/code reviews and incident retrospectives.

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.

Lead client-facing data platform architecture and strategy. Design scalable Databricks/Azure or AWS-based solutions, oversee multi-team delivery, incident response, governance, and data security. Mentor engineers, drive roadmap and budget decisions, and ensure resilient, high-quality data pipelines aligned to business goals.

Entry-level data engineer to follow a structured 12-month development programme. Build internal reporting, reusable engineering frameworks, documentation, and tooling while learning SQL, Python, Databricks and cloud data services. Support senior engineers, join code reviews, shadow customer workshops, and progressively contribute to client projects.

Design, build and maintain cloud-native ETL/ELT pipelines and data lake solutions using dbt, Snowflake/Databricks and AWS services. Optimize SQL and data models for performance and cost, create dashboards, follow CI/CD and software engineering best practices, and collaborate with architects, engineers and clients to deliver production-ready enterprise data solutions.

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.

The role involves developing data pipelines, managing metadata and data quality in Azure, ensuring compliance with data governance standards, and collaborating with data teams.

The Data Engineer designs and maintains data products and pipelines for analytics, working collaboratively with the Information Management Pod to ensure data integrity and performance.

As a Data Engineer, you will develop and manage data pipelines, ensuring reliable data ingestion and delivery while optimizing for simplicity and performance.

Design, build, and optimize cloud-based data platform components: ingest and transform varied datasets, create metadata-driven pipelines and data APIs, support AI/ML infrastructure, implement monitoring and metrics, and improve data processes to enable analytics and operational systems.

The Data Engineer will build and maintain data infrastructure, optimize performance pipelines, and ensure efficient data ingestion and querying for analysis within a high-performance AI media platform.

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.

Build and maintain BigQuery data models and Python data pipelines using Dataform and Airflow. Implement data quality checks, observability, and CI/CD; containerize workloads with Docker; support Data Science productionization and contribute to Looker reporting.

Design, build, maintain and evolve cloud data platform and ELT pipelines. Collaborate with stakeholders to provision, model and govern data, automate CI/CD, create data quality tests, and recommend new technologies.

Build production AI-powered tools, data pipelines, and internal full-stack applications that automate incident investigation, surface anomalies, and enable AI agents to interact safely with platform systems. Work across Python, TypeScript, Go and SQL, deploy on Docker/Kubernetes/AWS, and collaborate with TechOps, Connectivity, and Data teams to own, monitor, and improve shipped systems.

As a Data Engineer, you will design, build, and maintain data pipelines and infrastructure to support data-driven decision-making in a collaborative environment.

Design, build, and maintain scalable data pipelines and Airflow orchestration; deliver Databricks-based data engineering solutions; support data science workflows; collaborate via GitHub, code reviews, and shared engineering standards; ensure reliable, observable, and maintainable pipelines.