Top Data Engineer Jobs in Bristol

Develop and manage data pipelines for decision-making and data science applications. Apply strong data quality, flow, and organization practices while coding in Python and SQL. Use Snowflake, Azure Databricks, cloud platforms, associated data services, AI-enabled data practices, and DataOps concepts. Collaborate with clients and multidisciplinary teams, communicate clearly, solve complex problems, and share knowledge.

Own and enhance data intelligence products, BI solutions, dashboards, KPI frameworks, data governance, master data management, and workflow automation. Translate business objectives into actionable insights, scalable architectures, roadmaps, and measurable commercial outcomes. Analyze travel and meetings data, improve data integrity, document systems and processes, support client implementations, and collaborate with Sales, Finance, Technology, and leadership teams.

Designs, develops, tests, deploys, and supports Databricks-based data products and scalable ETL/ELT pipelines. Uses PySpark, Spark SQL, SQL, Python, Azure, and Power BI to build curated datasets for reporting, analytics, and operational decision-making. Responsibilities include data quality, reconciliation, monitoring, optimization, documentation, governance, production support, and collaboration with architects, analysts, and stakeholders.

Designs, builds, and operates Python-based data platforms, web applications, APIs, and agentic workflows. Develops batch and streaming pipelines, AWS data services, secure backend systems, CI/CD, monitoring, and governance controls. Translates business needs into production solutions, establishes reliable AI-agent safeguards, mentors engineers, reviews designs and code, and coaches business users building front ends with AI-assisted tools.

Develop and optimize Supabase’s Data API based on PostgREST. Responsibilities include improving the PostgreSQL driver and performance, developing features, fixing bugs, resolving incidents and escalated support issues, improving observability, and debugging integrations across the Supabase platform. The role requires strong Haskell development, PostgreSQL and SQL expertise, Linux debugging, open-source contribution, and technical user support.

Lead the technical direction of client data engagements, designing Microsoft Fabric and Azure data platforms, pipelines, models, testing, CI/CD, and operational practices. Manage and technically guide data engineers while remaining hands-on with complex challenges, incident response, and troubleshooting. Translate client requirements into technical roadmaps, collaborate with stakeholders, and ensure high-quality, commercially sound delivery using Python, PySpark, SQL, and modern data architecture patterns.

Designs, builds, and maintains production data pipelines using Microsoft Fabric, Python, and PySpark. Owns architecture, data modeling, CI/CD, reliability, troubleshooting, and technical decision-making. Collaborates with architects, product teams, and clients to translate requirements into robust solutions while mentoring junior engineers and improving engineering practices.

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.

Build, maintain, and release reliable cloud software for the Customer Data Platform. Lead complex feature delivery, make architectural decisions, improve code quality, testing, observability, security, and operational reliability. Collaborate with Product, Design, and adjacent engineering teams, participate in on-call support, contribute to planning and OKRs, and mentor engineers through pairing, reviews, and technical guidance.

Own the architecture, development, and deployment of scalable data pipelines using Snowflake, dbt, and Dagster. Design data and machine learning models, implement tiered data architectures, and establish data quality, governance, security, RBAC, and masking controls. Provide technical leadership across CI/CD, version control, orchestration, and Agile delivery while collaborating with product, analytics, and platform teams.

Design, build, deploy, and optimize scalable AI and machine learning systems using large datasets. Develop predictive models, recommendation systems, personalization engines, forecasting solutions, generative AI applications, and production ML pipelines. Collaborate with data, backend, and product teams to integrate models into applications, while monitoring performance, data quality, drift, reliability, and cost. Establish best practices for evaluation, experimentation, reproducibility, and deployment, and research emerging AI technologies.

Own production Python data pipelines, ETL/ELT workflows, and the ontology powering an AI platform. Integrate warehouses and business applications with graph databases and vector stores, while managing deployment tooling, cloud infrastructure, monitoring, and security. The role begins as hands-on individual contribution and develops toward technical leadership through mentoring and architecture ownership.

Build and operate scalable cloud data pipelines, warehouses, transformation layers, and ML production systems. Lead data infrastructure architecture, warehouse performance optimization, DataOps monitoring, CI/CD improvements, data quality testing, and large-scale data product delivery. Collaborate across teams, promote data stewardship, and support batch and streaming pipelines while deploying, serving, and monitoring machine learning models.

Build and deliver end-to-end data products on GCP, including data modelling, ETL/ELT, testing, validation, DevOps, governance, deployment, and go-live. Lead MDM strategy, architecture, product selection, integration, and implementation, including bespoke solutions and real-time data patterns. Provide senior technical leadership to multidisciplinary feature teams improving FDP data quality and consumption readiness.

Build end-to-end data applications, APIs, internal tools, and user interfaces using AI-assisted development. Work across Python backends, React frontends, data models, Databricks, dbt, pipelines, cloud infrastructure, testing, security, and performance. Review AI-generated code, establish guardrails, improve engineering workflows, and help shape application architecture and technical direction.

Build, scale, and operate Databricks and AWS infrastructure supporting automated, reproducible machine learning workflows. The role includes infrastructure-as-code with Terraform or Pulumi, containerized platforms using Docker and Kubernetes, system architecture, reliability and cost optimization, and cross-functional collaboration with data and ML teams. The engineer will lead complex projects, improve platform scalability and usability, document systems, and mentor peers while operating production-scale data and ML platforms.

Build and maintain data platforms, pipelines, lakes, warehouses, and integrated data models for analytics, reporting, data science, and AI solutions. The role involves hands-on client delivery, software engineering, data transformation, technical problem-solving, and advising clients. Engineers may lead teams or set technical direction and will occasionally work onsite with London-area clients.

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.

Build and maintain Python data ingestion pipelines for Reach’s AWS data lake, integrating APIs, S3, and SFTP sources. Design and operate AWS infrastructure, implement Infrastructure as Code and observability, and work with Athena, Glue, Lambda, ECS, and Redshift. Collaborate with analysts, product managers, and business teams, while documenting solutions, enforcing unit testing, and improving data quality and reliability.

Design, develop, and support scalable cloud-based data solutions across AWS and Azure. Build ETL/ELT pipelines, data integrations, migration processes, warehouses, data lakes, and lakehouses. Perform data profiling, quality assessments, validation, reconciliation, troubleshooting, and root-cause analysis. Create source-to-target mappings and technical documentation while supporting data governance, security, and compliance. Collaborate with technical and business stakeholders to deliver secure, high-quality data solutions.

Lead the design, deployment, and operation of scalable data pipelines and cloud infrastructure for Kpler’s marine insurance initiative. Build containerized AWS services using Python, Docker, Kubernetes, Airflow, and Terraform; manage PostgreSQL and Kafka/Flink streaming systems; establish observability standards; provide technical direction; collaborate cross-functionally; and mentor engineers.

Build and launch scalable data pipelines, products, APIs, and tools for proprietary market research. Develop big data processing systems, reusable software components, AI-ready data assets, cloud infrastructure, and governance controls. Partner with investment professionals, researchers, data scientists, and Compliance to deliver reliable, auditable, compliant solutions while optimizing system performance, cost, and reliability.

Designs, develops, and supports secure cloud-based data integration solutions using Microsoft Fabric and Azure. Responsibilities include client discovery, requirements gathering, ETL and ELT development, REST API and SFTP integrations, data pipeline implementation, production monitoring, incident investigation, and reliability improvements. The role collaborates with architects, analysts, project managers, and delivery teams across multiple client engagements.

Leads the design, development, testing, integration, and optimization of scalable data-processing and persistence components. Establishes engineering and operational standards, ensures pipeline performance and compliance, troubleshoots systems, supports proposals, and collaborates with architects, operations, security teams, customers, and managers. Coaches and develops engineers while overseeing code quality, data models, testing, deployment readiness, and technical delivery.

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.