Top Data Jobs in Bristol

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 technical direction and architecture for Arbor’s data platform, spanning transactional, analytical, and AI systems. Define target architectures, guide technical leads, support product delivery, and enable AI-first engineering through LLM orchestration, RAG, and real-time inference infrastructure. The role includes mentoring, architectural reviews, data governance, security, compliance, observability, DataOps, cloud platform cost optimization, and cross-functional collaboration with product and commercial leaders.

Embedded within the commercial team, the Senior Data Analyst will own core sales and revenue metrics, improve pipeline and forecast accuracy, investigate CRM data quality, size commercial opportunities, and analyze partner, retention, and renewal performance. The role translates ambiguous business questions into actionable recommendations, defines trusted metric frameworks, communicates insights to senior stakeholders, and increases self-service analytics adoption across non-technical commercial teams.

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.

Own the end-to-end design, reliability, resilience, and recovery of production data ingestion systems and pipelines. Build batch and streaming ETL/ELT workflows, establish data quality checks and SLAs, improve monitoring and alerting, resolve platform issues, reduce technical debt, and optimize performance and cost. Collaborate with stakeholders, AI teams, and international expansion initiatives while supporting time-series, textual, and event-stream data workloads.

Leads and develops a team of Data Scientists and other Data professionals, setting strategic direction and identifying high-impact opportunities. Partners with Product, Engineering, and senior stakeholders to shape product strategy and influence decisions through experimentation, causal inference, statistical modelling, machine learning, and analytics. Ensures analytical work drives measurable customer and commercial outcomes while collaborating with other data disciplines on tooling and infrastructure. Also contributes to hiring, coaching, technical standards, and the broader Data Science practice.

Lead a financial crime data science team focused on detecting, preventing, and mitigating financial crime. Partner with Product, Risk, Engineering, and control owners to shape data strategy, generate strategic insights, improve product decisions, and deliver rigorous analytics and machine learning solutions. Hire, develop, and retain data scientists and analysts while supporting innovation through business intelligence, experimentation, causal inference, and self-serve analytics.

Own the design, delivery, and operation of complex data pipelines, datasets, and platform components. Translate business needs into scalable data models and reliable data products, improve data quality and observability, resolve production issues, and establish reusable engineering tools and standards. Collaborate with product and business stakeholders, contribute to architecture decisions, mentor engineers, and participate in on-call support.

Operate and architect large-scale distributed data infrastructure across Kafka, Hadoop HDFS, Ceph, SQL Server, bare-metal systems, and hybrid cloud environments. Responsibilities include capacity planning, reliability engineering, automation, observability, incident response, backup and recovery, platform tooling, and architecture decisions. The role requires expertise with petabyte-scale systems or billions of daily events, mentoring engineers, and shaping infrastructure standards across engineering organizations.

Own ACLED’s digital growth and demand-generation programs, including email marketing, automated funnels, webinars, virtual events, product communications, digital campaigns, and sales collateral. Analyze campaign performance, optimize conversion and ROI, and translate conflict data products into compelling messaging. The role collaborates with sales, research, and market-focused leadership to expand audiences, generate pipeline, and convert users, partners, and customers. It is fully remote with approximately 15% global travel.

Design and develop cloud-based data platform solutions, including data pipelines, ingestion, transformation, cleansing, integrations, and data applications. Translate technical requirements into detailed designs, write reliable code, develop test automation, troubleshoot operational issues, tune batch and real-time processing, secure cloud data platforms, and implement CI/CD and monitoring. The role also involves advising client developers and defining efficient operational processes.

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.

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).

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.

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 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.

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, 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.