Top Data Jobs in Bristol

Reviews clinical trial data for quality, consistency, and completeness; identifies and resolves discrepancies in EDC systems; cross-checks patient data across EDC, laboratory, and safety databases; supports data cleaning, interim analyses, and database lock activities; monitors site data entry and escalates risks; collaborates with clinical, data management, and operations teams; and ensures compliance with GCP, study protocols, and regulatory requirements.

Visualise and analyse maintenance and operational performance across water supply assets and processes. Develop Power BI reports, performance measures, risk visualisations, and automated reporting to identify trends and support mitigation actions. Collaborate with subject matter experts and stakeholders, ensure data ownership and quality, promote cybersecurity and GDPR compliance, and provide insights that drive operational improvements across abstraction, treatment, transmission, pumping, and storage activities.

Lead multidisciplinary design teams delivering complex hyperscale, colocation, and enterprise data centre projects across Europe. Oversee projects from site due diligence and campus planning through detailed design and construction documentation. Ensure high-quality, timely outputs, manage risks, support commercial governance, maintain client relationships, coordinate permitting and localization requirements, and present to stakeholders. Regular travel across UK&I and European offices is required.

Analyze semiconductor manufacturing and test data across NPI, yield engineering, product quality, and high-volume production. Identify yield-loss mechanisms, process variation, failure signatures, outliers, and reliability issues; conduct root-cause investigations and statistical analyses; and develop automated scripts, dashboards, and repeatable workflows. Communicate findings through visualizations, reports, and presentations while collaborating with engineering teams and global manufacturing partners. Worldwide travel may be required.

Embedded within the commercial team, the Staff Data Analyst will own trusted metrics for conversion, contracted volume, sales, margins, pipeline, and forecasting. Responsibilities include improving forecast accuracy, diagnosing CRM and transactional data quality issues, analyzing commercial opportunities and partner performance, identifying risks and trends, and translating complex findings into recommendations for senior stakeholders. The role also promotes metric adoption and self-service analytics across non-technical commercial teams.

Leads the design and scaling of Databricks-based data, analytics, and AI platforms supporting global customer operations. Builds enterprise data pipelines, machine learning, NLP, generative AI, conversational AI, dashboards, and operational intelligence solutions. Establishes governance, data quality, CI/CD, reliability, and AI lifecycle standards while partnering with product, operations, engineering, workforce management, and AI teams. Provides technical leadership, coaching, and mentorship to engineers and data scientists.

Evaluate and annotate 3D meshes, GLB files, images, and short videos for model training and error resolution. Identify whether assets are artist-created or AI-generated, and detect geometry defects, topology errors, and scene inconsistencies. Requires hands-on 3D modeling experience, familiarity with Blender or Maya, strong mesh-topology knowledge, English proficiency, and a suitable computer with dedicated graphics capabilities.

Curate and interpret drug discovery data from scientific papers, patents, and other disclosures. Responsibilities include interpreting chemical structures and biological data, building and maintaining databases, researching biopharmaceutical R&D, and surfacing accurate insights for scientific and industry audiences. The role requires strong data curation, patent literature, chemoinformatics, scientific communication, and attention to detail skills.

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.

Design, build and maintain data platforms, pipelines, lakes/warehouses and analytical models. Deliver client-facing solutions, advise on technical direction, lead teams, and implement robust, maintainable data engineering systems for analytics and ML training.

Design and build data platforms, pipelines, lakes and warehouses; ingest, transform and model data for analytics and ML; lead teams, set technical direction, and work client‑facing (ad‑hoc onsite in London).

The Product Manager for Data will lead the rollout of data products for clients, overseeing requirements, development support, adoption, and communication while contributing to a global data model.

As a Software Engineer, you'll manage data collection and infrastructure for AI model training, collaborating with scientists and stakeholders to enhance data processing workflows.

Owns site maintenance planning, scheduling, work-order lifecycle management, CMMS administration, preventive maintenance programs, asset lifecycle planning, compliance controls, vendor performance, and KPI reporting. The role coordinates engineering teams, vendors, stakeholders, and global operations to maintain uptime, ensure audit readiness, implement change controls, and drive data-led maintenance improvements across data-center sites.

Lead the governance, quality, standards, and continuous improvement of global customer and supplier master data. Develop MDM frameworks, processes, documentation, metrics, and training; partner with business, IT, process owners, and third-party providers; monitor service-level performance; support transformation projects; and manage MDM analysts.

Lead the global Finance and Product master data management function during ERP transformation. Establish governance frameworks, standards, policies, procedures, and data quality metrics; improve master data integrity across business systems; and support business transformation initiatives. Manage and develop two MDM Analysts, collaborate with stakeholders, IT, and third-party providers, document processes, and deliver user training.

The role involves leading data engineering projects in the public sector, creating technical designs, implementing solutions, and managing teams to drive technical outcomes for customers.

The role involves designing and implementing Master Data Management solutions, assessing enterprise data domains, and defining governance structures, requiring strong consulting skills and Azure expertise.

Lead the design, evolution, and operation of enterprise data platforms, pipelines, transformation frameworks, and analytical datasets. Establish engineering standards for quality, monitoring, governance, security, CI/CD, testing, and automation. Mentor data engineers, guide architecture and technology decisions, shape data roadmaps, and partner with analytics, BI, architecture, and business stakeholders. Ensure scalable, reliable, cost-effective data solutions support reporting, advanced analytics, AI, and digital transformation.

Lead mechanical delivery for hyperscale and co-location data centre projects across UK/EMEA. Drive technical excellence, client relationships, work winning, project financials, team leadership, and represent AECOM in the data centre community.

Lead Data & AI strategy engagements for public-sector and regulated clients: assess maturity, prioritise use cases, build business and investment cases, design transformation roadmaps, advise on technology strategy, and support sales, delivery and capability building. Act as trusted advisor to senior stakeholders and mentor consultants to move clients from experimentation to scaled delivery.

Wayvia is building a talent pipeline for engineering roles focusing on backend systems, data pipelines, and product engineering. They seek curious and collaborative engineers to connect with and solve future challenges.

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 troubleshoot scalable AWS data pipelines using Python, PySpark, Spark, and Airflow. Perform root-cause analysis of pipeline and data-quality issues, apply dimensional modeling and slowly changing dimensions, and deliver governed data assets. Provision infrastructure with Terraform, containerize solutions with Docker, and manage GitLab CI/CD deployments. Work with AWS security, encryption, IAM, monitoring, and analytics services in a regulated government environment.

Migrate SQL Server data products and analytical workloads to a modern cloud platform. Redesign legacy SQL, ETL processes, and business logic; build scalable pipelines and analytical models; validate data quality; collaborate with stakeholders; and improve platform reliability, performance, cost efficiency, and AI-assisted data engineering automation.