Top Data Engineer Jobs in Bristol

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.

Designs, builds, operates, and supports secure, governed data pipelines and services for reporting, integration, migration, and analytics. Responsibilities include ingestion, transformation, validation, publishing, data modeling, quality controls, lineage, metadata, access controls, migration, monitoring, troubleshooting, and documentation. The role collaborates with analysts, architects, developers, and business stakeholders across cloud data environments.

Leads engineering strategy, architecture, delivery, and productionisation for a secure cloud Data Intelligence Platform. Designs ingestion, streaming, transformation, lakehouse, serving, API, and feature-pipeline capabilities while establishing data quality, governance, lineage, observability, CI/CD, infrastructure-as-code, resilience, and FinOps standards. Coordinates analytics, ML, security, infrastructure, suppliers, and operations; manages technical priorities and risks; supports governance decisions; and mentors data engineers.

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.

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.

Build, maintain, and troubleshoot scalable AWS data pipelines using Python, PySpark, Spark, and Airflow. Perform root-cause analysis of pipeline and data-quality issues, apply dimensional modeling and slowly changing dimensions, and deliver governed data assets. Provision infrastructure with Terraform, containerize solutions with Docker, and manage GitLab CI/CD deployments. Work with AWS security, encryption, IAM, monitoring, and analytics services in a regulated government environment.

Lead customer underwriting proof-of-concept projects using SQL, Python, and AWS to transform, validate, and deliver insurance risk data. Match address data to reference identifiers, investigate data-quality issues, document outputs, support underwriting data platforms, and automate manual workflows. Collaborate with technical and non-technical stakeholders, communicate project updates, and improve scalable data processes, quality, and customer experience.

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.

Design and maintain data models and Databricks pipelines to support change control and as-built configuration. Develop Databricks SQL/Python solutions and Power BI dashboards, align business requirements with reporting, liaise with IT stakeholders, and support team upskilling toward data-centric ways of working.

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.

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.

Lead the design and delivery of scalable data platforms, pipelines, models, and integrations. Oversee ingestion, transformation, governance, security, monitoring, and operational reliability across client projects. Collaborate with analysts, data scientists, architects, project managers, and stakeholders to translate requirements into technical solutions. Support predictive model deployment, prototype emerging technologies, and mentor data engineers through coaching, code reviews, and knowledge sharing.

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, 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.

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.

Leads the design and scaling of Databricks-based data, analytics, and AI platforms supporting global customer operations. Builds enterprise data pipelines, machine learning, NLP, generative AI, conversational AI, dashboards, and operational intelligence solutions. Establishes governance, data quality, CI/CD, reliability, and AI lifecycle standards while partnering with product, operations, engineering, workforce management, and AI teams. Provides technical leadership, coaching, and mentorship to engineers and data scientists.

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).

As a Software Engineer, you'll manage data collection and infrastructure for AI model training, collaborating with scientists and stakeholders to enhance data processing workflows.