Top Data Engineer Jobs in Bristol

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 maintain FinOps data pipelines, BigQuery and dbt models, cost attribution systems, forecasting and anomaly detection models, and cost-versus-revenue analytics. Identify and implement AWS and GCP savings opportunities, automate governance with Python, Terraform, and Lambda, and partner with Engineering, Finance, Product, and leadership on cloud costs, forecasting, reporting, and architecture decisions.

Develop and maintain data pipelines, models, ETL workflows, validation processes, and interfaces for defence modelling and simulation data. Transform complex simulation outputs into reliable datasets for experimentation, analysis, visualisation, and capability assessment. Collaborate with modelling and simulation engineers, software engineers, analysts, and domain specialists. The role requires Python, SQL, data-quality practices, and the ability to work in offline or restricted environments supporting UK Ministry of Defence projects.

Develop and maintain data pipelines, models, ETL workflows, and validation processes for defence modelling and simulation data. Integrate simulation and analytical environments, automate repeatable processing, and make complex datasets accessible for experimentation, analysis, and visualisation. The role requires Python, SQL, data modelling, and experience working in offline or restricted environments, supporting UK Ministry of Defence projects.

Designs and maintains secure, scalable data pipelines for defence, government, and national security customers. Responsibilities include ETL development, data profiling, modelling, quality controls, governance, anonymisation, cloud storage, workflow optimisation, and support for analytics and visualisation. The role also contributes to data engineering strategy, standards, research, and stakeholder presentations across the full project lifecycle.

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.

Build and improve Power BI dashboards, data models, measures, and reports. Translate business questions into actionable insights, validate and reconcile SQL data, identify trends, improve data quality, consolidate reporting, and support governance, security, and deployment standards. Collaborate with IT, Finance, and other stakeholders while enabling users to understand and trust business intelligence solutions.

Designs and implements enterprise cybersecurity capabilities across cloud, AI, applications, infrastructure, and data. Responsibilities span security architecture, LLM and agentic AI security, red teaming, threat modeling, DevSecOps, secure CI/CD, detection engineering, threat hunting, SIEM/SOAR automation, security data science, knowledge graphs, and advanced security testing.

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.

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

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 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.

Leads enterprise data engineering strategy and delivery across Databricks, dbt, Azure, Python, and SQL. Defines architecture and engineering standards while driving modernization, automation, security, resilience, governance, and compliance. Influences stakeholders, coaches engineering teams, simplifies technology landscapes, and aligns data, cloud, analytics, and AI investments with business outcomes in a regulated environment.

Build and scale governed data products, pipelines, analytical models, semantic layers, APIs, and AI-enabled interfaces. Establish standards for data modeling, orchestration, testing, observability, CI/CD, security, and lifecycle management. Partner with stakeholders to define data contracts and product roadmaps, deliver solutions end to end, improve analytics governance, and mentor colleagues. The role requires strong data and analytics engineering expertise, product judgment, and practical experience directing AI coding agents.

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.

Own and expand third-party billing and usage integrations for a cloud cost platform. Build and maintain ingestion pipelines, normalize vendor data, manage orchestration and backfills, and ensure financial-grade correctness through reconciliation, deduplication, and data quality checks. Use AI throughout the engineering workflow, collaborate with product and support teams, and improve integration scalability. The role requires strong SQL and Python, cloud data warehouse experience, REST API integration expertise, and production pipeline ownership.

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.

Own the company’s end-to-end data platform, including ingestion, S3 data lakes, ClickHouse, dbt transformations, orchestration, and BI/CRM data delivery. Improve reliability, historical accuracy, cost, and metric governance while defining and defending business KPIs. Partner with Customer Success, Sales, Product, and leadership to translate business questions into durable metrics and establish data standards. This is a senior individual contributor role focused on hands-on engineering, analytical rigor, and stakeholder communication.

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.