Top Data Engineer Jobs in Bristol

Analyze and transform fleet and engineering data into actionable insights, support data exploitation decision-making, and develop capabilities that improve business and programme performance. The role collaborates with engineering disciplines and senior stakeholders to standardize methods across projects. Additional opportunities include quality improvement, safety initiatives, mentoring, coaching, STEM outreach, and company growth projects.

Designs and maintains scalable ETL and ELT pipelines, data architectures, databases, and warehouses. Integrates data from APIs and third-party platforms while ensuring quality, security, governance, and reliability. Monitors workflows, improves processing and query performance, supports analytics and business intelligence initiatives, and documents technical processes. Collaborates with analysts, engineers, data scientists, and business stakeholders in a fully remote environment.

Design, develop, and maintain scalable data pipelines and architectures for analytics and reporting. Perform ETL and streaming data integration, data modeling, governance, quality management, and workflow optimization across cloud and big data environments. Translate business requirements into data solutions, monitor reliability and performance, and collaborate with stakeholders to advance Walmart’s data strategy.

Provide technical leadership for ENSEK’s data ecosystem, owning data architecture, platform strategy, roadmap, and data mesh adoption. Establish standards for event-driven architecture, schema governance, data quality, observability, lineage, metadata, security, and cost management. Design governed data-sharing interfaces for customers and partners, solve complex engineering challenges, influence senior stakeholders, and mentor engineering teams.

Designs, develops, tests, and supports reliable data engineering solutions for insurance data products. Responsibilities include data transfers, profiling, validation, quality management, issue resolution, data modeling, workflow management, code reviews, troubleshooting, and automation. The role collaborates with technical teams and business stakeholders to translate requirements into scalable, maintainable solutions while exploring AI-assisted engineering practices.

Own EULER’s data lifecycle, including instrumentation, validation, reconciliation, privacy, anonymization, canonical data modeling, analytics datasets, documentation, and production pipelines. The role requires strong SQL, software engineering practices, independent ownership, skepticism toward data quality, and clear communication. Preferred experience includes GDPR/LGPD, multi-tenant B2B SaaS, dbt, statistics, Cloudflare Workers, Postgres, Parquet, DuckDB, and Bubble.

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 the technical direction of data engineering across client engagements. Design migration frameworks, streaming architectures, Lakehouse platforms, data integrity systems, and regulatory reporting pipelines. Provide architectural authority, review technical designs, select cloud services, mentor senior engineers, develop reusable delivery patterns, support hiring, and represent the consultancy externally. Shape proposals, scope technical engagements, present to senior stakeholders, and identify opportunities with existing clients.

The Senior Data Platform Engineer will manage Databricks infrastructure, develop cloud systems, collaborate with teams, and enhance machine learning workflows.

Design and coordinate electrical services for data centre and industrial facility projects. Manage electrical delivery on small and mid-sized projects, contribute technical expertise to studies and proposals, coordinate with multidisciplinary teams, present solutions, conduct site inspections, and liaise with clients, contractors, suppliers, and consultants. The role requires building services design experience, professional accreditation progress, knowledge of relevant standards, and willingness to travel internationally when required.

Leads mechanical engineering delivery for complex data centre projects across multidisciplinary and geographically distributed teams. The role involves technical leadership, project delivery, collaboration with technology clients, permitting and regulatory understanding, construction methodology, and strategic thought leadership for AECOM’s data centre practice. Candidates should have relevant engineering or architecture qualifications, data centre design experience, Revit knowledge, and Uptime Institute accreditation. Travel may be required.

Leads electrical engineering and construction administration for large-scale data centre projects. Responsibilities include project planning, electrical design, contract documentation, cost control, technical reporting, client and contractor liaison, site inspections, design coordination, proposal development, and mentoring junior engineers. The role requires chartered-level expertise, data centre design experience, multidisciplinary team leadership, and familiarity with electrical design software and industry regulations.

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 scalable ETL pipelines, data models, ingestion processes, and optimized data products using Azure Databricks and related Azure services. Develop raw, refined, and curated data layers, implement testing and secure ETL designs, monitor pipeline performance, resolve incidents, document solutions, and participate in Agile delivery and governance. Collaborate with architects, analysts, engineers, and stakeholders on modern data platform development.

Provides regional technical authority for BMS, EPMS, PLC, OT, and DCIM architectures across data centres. Defines engineering standards, governs complex designs, resolves high-impact technical risks, leads vendor and product strategies, and mentors engineering teams. Advises executives on technology roadmaps and ensures reliable, scalable mission-critical infrastructure.

Build AI-ready data infrastructure by designing event tracking plans, developing dbt models, and managing ingestion pipelines into Snowflake. Own data quality, metric definitions, documentation, semantic layers, and reporting consistency across Amplitude and Snowflake. Orchestrate workflows with Airflow and collaborate with product, engineering, and analytics teams to deliver reliable data products.

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.

Build and operate OrderYOYO’s production data and AI platform, including Microsoft Fabric lakehouse solutions, scalable ingestion pipelines, semantic models, monitoring, data quality, and legacy-platform migrations. Develop with Python, PySpark, SQL, and T-SQL; manage performance, cost, and reliability; and collaborate with Finance, Product, executives, and engineering. Deliver applied AI and machine-learning capabilities such as RAG, agents, churn, recommendations, personalization, fraud, and risk models.

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.

Design, build, and optimize Phantom’s streaming blockchain data platform, including scalable ETL pipelines, data models, schemas, quality frameworks, observability, and high-availability systems. The role delivers accurate, low-latency token, price, and spam data to engineering teams, collaborates cross-functionally, and contributes to team growth. Experience with backend systems, modern data engineering, and Web3 or on-chain data is expected.

Build interactive data visualizations, maps, dashboards, and 3D experiences that turn complex energy data into clear narratives. Work independently with marketing, policy, press, leadership, and data science teams using React, Next.js, Mapbox, WebGL, and Blender. Own projects end to end, select appropriate visualization techniques, communicate insights effectively, and rapidly adapt to varied project requirements.

Design and maintain reliable data solutions, lead a squad of engineers, develop scalable data pipelines, and ensure adherence to data standards.

Own the end-to-end delivery of applied machine learning products, from framing customer problems and building data pipelines to deploying, monitoring, and measuring model impact. Develop models for operational data using Python, SQL, dbt, Snowflake, and AWS; define evaluation and drift monitoring; conduct A/B tests or causal analyses; and partner with product and engineering teams to embed ML into customer workflows.

Own and develop Azure Databricks data-platform workstreams across data modelling, medallion architecture, Delta tables, data quality, infrastructure, CI/CD, DevOps, security, and governance. The role requires autonomous delivery from scoping through production, troubleshooting Azure infrastructure, managing Terraform and Azure DevOps, protecting PII, and engaging technical and business stakeholders globally.

Own and modernize end-to-end financial data pipelines, predictive models, and cloud infrastructure. Build real-time market-data ingestion, migrate services to AWS, rewrite PHP calculations in Python, and manage time-series and search databases. Implement data quality, monitoring, alerting, containerized deployments, CI/CD, and GitOps workflows. Collaborate with quantitative researchers and analysts while mentoring engineers and guiding architecture.