Top Data Jobs in Bristol

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 and maintain production data pipelines and features in Python; own project lifecycle from design to reliable production; set technical direction for the Insights platform; improve engineering standards (testing, observability, data quality); apply AI across products and pipelines; help establish semantic/AI-ready data layers.

Lead UK sales strategy for data center customers, develop strategic relationships with hyperscalers and colocation providers, manage pipeline and forecasting, coordinate cross-functional pursuit teams, and drive profitable growth through customer engagement, specification, negotiation and long-term account development.

Lead end-to-end SAP S/4HANA data migration activities: define strategy and governance, manage mapping, cleansing, ETL and reconciliation, execute mock and production loads, resolve defects, coordinate with testing and business teams, and support cutover readiness.

Customer-facing technical leader owning long-term technical success and value realization for strategic enterprise accounts. Architect and lead implementations, migrations, and scalable cloud-native deployments; build joint success plans; monitor adoption and health; partner with Sales, Product, and Engineering; and provide technical guidance, reference architectures, and feedback to drive predictable expansion and customer outcomes. Travel to customer sites as needed.

Leads the Payments data strategy and partners with Product, Engineering, and Design to shape payment products, commercial optimization, customer growth, metrics, architecture, and operating models. Builds and scales a high-performing, full-stack data organization spanning data science, analytics engineering, machine learning engineering, and analytics. Serves as a trusted Payments Leadership partner, translating technical, product, and commercial perspectives into high-impact decisions.

Build production-grade AI agents, integrations, data pipelines, and infrastructure directly within strategic customer environments. Engineers rapidly navigate unfamiliar codebases and data stacks, own technical delivery through production deployment and handoff, communicate credibly with customer engineering teams, document and train users, and feed recurring engagement insights back to Product and Engineering. The role requires 30–50% travel during active engagements and emphasizes backend systems, agent integrations, real-world deployments, and maintainable customer outcomes.

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.

Develop and deploy machine learning and statistical models that solve business problems and create measurable impact. Responsibilities include exploratory data analysis, feature engineering, experimentation, model optimization, explainability, MLOps, prompt engineering, and cloud-based data collaboration. The role involves working with clients and stakeholders, communicating insights through visualization and storytelling, and contributing to scalable analytics, Generative AI, and data science solutions.

Lead enterprise-scale data science and AI initiatives, defining lifecycle best practices, architecture, governance, metadata frameworks, and MLOps practices. Design and deploy machine learning, statistical, optimization, and LLM solutions using cloud platforms. Lead multidisciplinary teams, translate business needs into analytical solutions, collaborate with engineers and stakeholders, and communicate insights to technical and executive audiences.

QA Engineer responsible for planning and executing manual and automated testing across an energy-focused SaaS data platform. Duties include test automation, exploratory, regression, acceptance, and performance testing; API and web application testing; defect reporting and root cause analysis; test coverage reporting; risk identification; and collaboration with engineering, product, support, and user teams to ensure reliable releases.

Lead electrical design and delivery for data centre projects across feasibility to handover. Define power architectures, run power system and protection studies (ETAP/SKM), integrate BMS/EPMS/SCADA, embed decarbonization and energy-efficiency measures, ensure compliance with standards, coordinate multidisciplinary interfaces, mentor teams, shape strategy and support business development.

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.

Take end-to-end ownership of backend features and SaaS services in a microservices architecture. Design, implement, deploy, and operate scalable, reliable Node.js services. Work with Redis Streams, MongoDB, Kubernetes, and ensure test coverage, performance tuning, and collaboration with cross-functional teams.

Hands-on role ensuring completeness, quality, and accuracy of product data for multi-region launches. Perform data validation, stewardship, QA, syndication (PIM), reporting, and stakeholder coordination to resolve discrepancies and track launch readiness.

Design, build, test and document secure batch and real-time data pipelines, perform logical/physical database design and data modelling, expand platform capabilities, analyse and resolve data processing issues, and ensure alignment with data architecture for downstream consumers.

Build, test, and document secure batch and real-time data pipelines for a government client; design logical and physical databases; extend platform capabilities; ensure data models align with architecture; perform data analysis and root-cause investigations to improve processing and availability for downstream consumers.

Designs and governs enterprise data architectures supporting analytics, reporting, operations, and AI initiatives. Responsibilities include data modeling, warehousing, integration pipelines, APIs, governance, quality, lineage, security, and architecture optimization. The role provides technical leadership and mentorship, guides cloud and big data technology decisions, develops proofs of concept and roadmaps, and supports client proposals and account growth. Collaboration with engineers, analysts, developers, and business stakeholders is central to delivering scalable, secure, high-performance data platforms.

Build and operate shared AI platform infrastructure for LLM applications, agents, RAG, tool calling, and AI workflows. Develop data and knowledge pipelines, secure enterprise integrations, reusable frameworks, deployment and observability standards, and developer tooling. Take AI systems from experimentation to reliable production while ensuring security, privacy, governance, scalability, quality, and cost efficiency. Collaborate across Engineering, Product, and IT to establish practical AI platform patterns and guardrails.

Support development and continuous improvement of data products for electricity transmission network planning and operations. Define roadmaps, manage backlogs, gather requirements, validate data quality and lineage, collaborate with stakeholders and delivery teams, enable analytics and governance, and measure product performance and adoption.

Lead business discovery and requirements analysis for data, reporting and analytics initiatives. Define processes, KPIs, data models, lineage and governance. Collaborate with analytics, data architects and delivery teams to elicit, validate and prioritise requirements, support testing, adoption and continuous improvement while managing stakeholders and multiple concurrent initiatives.

Lead enterprise data governance by defining frameworks, policies, and operating models. Manage data quality, metadata, catalogs, lineage, stewardship, compliance, and MDM. Collaborate with stakeholders, run governance councils, track KPIs, and support analytics, cloud, and AI initiatives.

Develop stored procedures or notebooks to build 230 additional data items into DCoE tables/views. Create an extensible, config-driven process to add fields with minimal re-engineering. Produce self-documenting, well-commented code and collaborate with the DCoE team on delivery; DCoE handles business analysis, design sign-offs, data modeling, and testing.

The Data Scientist supports customer use of the Epistemix platform through data integration, modeling, and visualization, improving decision-making and customer satisfaction.

The Senior Data Engineer will enhance backend systems for a social media app by managing databases, optimizing recommendation algorithms, and ensuring accurate recommendations.