Top Data Engineer Jobs in Bristol

Designs and delivers scalable data pipelines and Lakehouse architectures using Databricks, Spark, PySpark, and Delta Lake. The role covers ETL/ELT, data governance, security, quality, performance optimization, cloud integration, and CI/CD. It involves technical leadership across customer programmes, architectural decision-making, stakeholder collaboration, mentoring, and supporting reliable production data solutions.

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 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.

Build, test, optimize, and maintain ELT pipelines supporting analytics, reporting, and data products. Deploy config-driven workflows to Databricks across a multi-tenant environment, extend data models, support CI/CD and ingestion projects, monitor pipeline health, troubleshoot failures, and collaborate with data and product teams to deliver reliable, well-tested data.

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.

Develop and maintain enterprise Power BI and Tableau reporting solutions, data pipelines, models, dashboards, and analytics using Snowflake, SQL, dbt, and Alteryx. Analyze institutional and external data to support strategic planning, forecasting, recruitment targets, and executive decision-making. Ensure data quality, reporting governance, release standards, and stakeholder satisfaction. Provide technical guidance, training, and support across the University, contribute to emerging AI-enabled analytics, and potentially line-manage junior developers.

Lead the technical direction and architecture for Arbor’s data platform, spanning transactional, analytical, and AI systems. Define target architectures, guide technical leads, support product delivery, and enable AI-first engineering through LLM orchestration, RAG, and real-time inference infrastructure. The role includes mentoring, architectural reviews, data governance, security, compliance, observability, DataOps, cloud platform cost optimization, and cross-functional collaboration with product and commercial leaders.

Own the end-to-end design, reliability, resilience, and recovery of production data ingestion systems and pipelines. Build batch and streaming ETL/ELT workflows, establish data quality checks and SLAs, improve monitoring and alerting, resolve platform issues, reduce technical debt, and optimize performance and cost. Collaborate with stakeholders, AI teams, and international expansion initiatives while supporting time-series, textual, and event-stream data workloads.

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.

Own the design, delivery, and operation of complex data pipelines, datasets, and platform components. Translate business needs into scalable data models and reliable data products, improve data quality and observability, resolve production issues, and establish reusable engineering tools and standards. Collaborate with product and business stakeholders, contribute to architecture decisions, mentor engineers, and participate in on-call support.

Operate and architect large-scale distributed data infrastructure across Kafka, Hadoop HDFS, Ceph, SQL Server, bare-metal systems, and hybrid cloud environments. Responsibilities include capacity planning, reliability engineering, automation, observability, incident response, backup and recovery, platform tooling, and architecture decisions. The role requires expertise with petabyte-scale systems or billions of daily events, mentoring engineers, and shaping infrastructure standards across engineering organizations.

Serve as the technical authority for enterprise customers in data security, owning the technical sales process. Deliver technical presentations, demos, and whiteboard sessions, advise on AI/ML-driven data protection, and drive outcomes through value-based selling with partners and GTM teams.

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

Analyze semiconductor manufacturing and test data across NPI, yield engineering, product quality, and high-volume production. Identify yield-loss mechanisms, process variation, failure signatures, outliers, and reliability issues; conduct root-cause investigations and statistical analyses; and develop automated scripts, dashboards, and repeatable workflows. Communicate findings through visualizations, reports, and presentations while collaborating with engineering teams and global manufacturing partners. Worldwide travel may be required.

The Data Engineer will design and support data warehouses, create analytics dashboards, perform data quality analysis, and identify process improvements. Responsibilities include collaborating with stakeholders to develop insights and ensure data governance.

The Data Engineer will build and manage scalable data pipelines and databases, collaborate on architecture, and drive data systems towards real-time capabilities in a fast-paced, innovative environment.

Design, build and maintain data platforms, pipelines, lakes/warehouses and analytical models. Deliver client-facing solutions, advise on technical direction, lead teams, and implement robust, maintainable data engineering systems for analytics and ML training.

Design and build data platforms, pipelines, lakes and warehouses; ingest, transform and model data for analytics and ML; lead teams, set technical direction, and work client‑facing (ad‑hoc onsite in London).

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.

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.

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.