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.

As a Python and Kubernetes Software Engineer, you will develop data analytics solutions using open source tools, collaborating with a distributed team.

The Platform Data Analyst will enhance visibility and performance optimization of Runware's media AI platform through analytics, monitoring, and reporting. Responsibilities include tracking performance, building dashboards, and collaborating with engineering and DevOps teams to improve observability and reliability.

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.

Lead a team in developing scalable data solutions, mentor engineers, manage expectations, and contribute to code. Focus on high-quality software practices.

The role involves programming and data review for real-world data analysis, supporting epidemiologists, and managing complex data sets using statistical programming languages and tools.

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.

Build and maintain scalable data pipelines and platform components to serve analytics, ML, and product teams. Design data services/APIs, ensure data quality, observability, security and compliance, and evolve cloud-native infrastructure using IaC and Kubernetes. Partner with cross-functional teams, produce documentation, and ship reliable, tested data solutions.

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.

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.

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.

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.

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.

Own end-to-end product analytics for the Pro team: build production-grade pipelines and dbt models, define north-star metrics, design A/B tests and causal inference frameworks, create dashboards and metric frameworks, embed AI tooling, and translate insights into product recommendations for leadership.

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.

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.

Support development, execution and optimisation of multi-channel media campaigns (AV, social, digital, OOH). Coordinate with agencies and stakeholders, monitor performance against KPIs, prepare reports/dashboards, manage budgets, implement testing and learning, and recommend optimisations to improve media effectiveness and ROMI.

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

Manage a multifunctional clinical operations/data review team in an FSP environment, ensuring quality deliverables, KPI compliance, customer issue escalation, resource planning, revenue impact, and people leadership including performance management, training, workload oversight, and staff development.

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.

Build production AI-powered tools, data pipelines, and internal full-stack applications that automate incident investigation, surface anomalies, and enable AI agents to interact safely with platform systems. Work across Python, TypeScript, Go and SQL, deploy on Docker/Kubernetes/AWS, and collaborate with TechOps, Connectivity, and Data teams to own, monitor, and improve shipped systems.

Lead analytics to improve player experience, engagement, retention, and monetization. Analyze player lifecycle, run cohort and funnel analyses, design and evaluate A/B tests, build dashboards, define product metrics, investigate KPI changes, and partner with product, marketing, engineering, and data teams to deliver data-driven recommendations and improve data quality.