Top Data Jobs in Bristol

Lead global market intelligence for Iron Mountain Data Centers by analyzing macro trends, cloud architectures, and power constraints; build GIS-based site selection tools; embed market data into underwriting and financial models; deliver investment recommendations and standardized market frameworks to executive leadership.

Build and deliver end-to-end data products on GCP, including data modelling, ETL/ELT, testing, validation, DevOps, governance, deployment, and go-live. Lead MDM strategy, architecture, product selection, integration, and implementation, including bespoke solutions and real-time data patterns. Provide senior technical leadership to multidisciplinary feature teams improving FDP data quality and consumption readiness.

Leads Pure DC’s project management function across customer fit-outs, construction-to-operations handovers, lifecycle capital programmes, and internal projects. Owns customer relationships, budgets, contractor procurement, change management, commercial growth, risk, compliance, and delivery quality. Manages regional project teams and third-party partners across multiple geographies while establishing scalable governance, processes, and project controls.

Build and maintain data platforms, pipelines, lakes, warehouses, and integrated data models for analytics, reporting, data science, and AI solutions. The role involves hands-on client delivery, software engineering, data transformation, technical problem-solving, and advising clients. Engineers may lead teams or set technical direction and will occasionally work onsite with London-area clients.

As a Senior Data Science Engineer, you'll lead data science projects, develop algorithms, analyze datasets, and mentor junior team members to enhance trading operations and strategies.

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.

Manage international third-party data providers supporting identity verification, fraud prevention, KYC/eKYC, document verification, and compliance products. Responsibilities include vendor sourcing, due diligence, onboarding, contract and usage documentation, data quality and performance evaluations, integration coordination, issue resolution, renewals, cost tracking, and process improvement. Collaborate with Product, Data Science, Engineering, Legal, Compliance, Procurement, and external partners across global markets.

Leads mechanical engineering delivery for hyperscale and colocation data centre projects across the UK and EMEA. Responsibilities include directing project workstreams, ensuring technical excellence, managing project financials, strengthening client relationships, supporting work winning, representing AECOM externally, and developing engineering teams. The role requires extensive data centre and mission-critical project experience, Chartered Engineer status, client-facing expertise, and strong leadership and communication skills.

Develop and deploy complex machine learning and analytics models, process large datasets, and deliver solutions through production. The role covers the full data analytics pipeline, including data ingestion, cleaning, feature extraction, modeling, validation, visualization, and reporting. The data scientist will collaborate with clients, software engineers, underwriters, and globally distributed teams while maintaining high-quality, maintainable code and continuing technical development.

Leads complex enterprise customer engagements as a senior technical authority for data and AI architecture. Drives Databricks adoption, consumption strategy, executive relationships, competitive positioning, and large-scale architecture initiatives including migrations, real-time systems, lakehouse platforms, and AI/ML. Orchestrates cross-functional teams, mentors solutions architects, influences senior stakeholders, and contributes to technical thought leadership.

Own production NLP and applied AI systems that enrich scientific literature with entities, classifications, claims, and summaries. Design and evaluate classical NLP, embedding, retrieval, and LLM approaches; build scalable Python pipelines; manage cost, concurrency, reliability, and model quality. Collaborate with data engineers on Airflow and Dagster orchestration, contribute to agentic AI applications, and work with editors, product managers, and engineers to deliver research intelligence to users.

Build and operate NLP enrichment pipelines over scientific literature, extracting entities, classifications, claims, and summaries. Compare classical NLP, retrieval, embedding, LLM, and fine-tuned model approaches using evaluations, cost, quality, and operational metrics. Create golden datasets, define evaluation strategies, contribute to agentic AI applications, and collaborate with editors, product managers, engineers, SMEs, and vendors to deliver production modeling systems.

Build Paddle’s data science function and production decisioning systems from the ground up. Prioritize high-value use cases, personally deliver the first ML or agentic system, and own deployment, monitoring, experimentation, reliability, governance, and financial impact. The role will establish the production ML stack, collaborate across Product, Engineering, Risk, Finance, and Compliance, then hire and lead data scientists and ML engineers. Experience with regulated transactional domains, production ML operations, experimentation, and executive stakeholder management is required.

Own the data platform product roadmap, including multi-chain ingestion, on-chain parsing, token and market data, APIs, datasets, analytics tools, governance, quality monitoring, and metric frameworks. Partner with engineering, backend, algorithm, BI, risk, growth, and business teams to deliver reusable data products. The role requires substantial product management experience, hands-on Web3 knowledge, understanding of blockchain data models, SQL proficiency, and familiarity with data infrastructure and analytics tooling.

Design, build, and own scalable data pipelines from ingestion through analytics and incentive payouts. Lead data platform architecture, including Snowflake, dbt, Iceberg, and S3 workflows. Establish data quality, lineage, observability, performance, cost, and SLA standards. Partner with engineering and analysts to support telematics accuracy and driver incentive reporting. Develop maintainable data models and transformation layers while using AI coding tools to improve development speed and quality.

Build and evolve CluePoints’ enterprise data platform using Microsoft Fabric and Azure. Responsibilities include migrating Azure SQL and ADF workloads, integrating SaaS and web data, developing scalable ingestion and transformation pipelines, maintaining Kimball dimensional models, and establishing CI/CD, data quality, lineage, security, and monitoring. The role partners with analytics leadership and supports trusted Power BI reporting across multiple business functions.

The Enterprise Account Executive will drive new enterprise revenue, engage with senior decision-makers, and execute growth strategies for Data and AI training solutions.

Collect, clean, validate, and analyze data using Excel, SQL, Power BI, and Microsoft Fabric. Create dashboards, reports, visualizations, KPI reporting, and actionable recommendations for business and client decision-making. Collaborate with analysts, consultants, and stakeholders, present findings clearly, maintain reporting solutions, and support data quality and project delivery. The role offers early-career development in data analytics and consulting.

Designs and delivers scalable data pipelines and Lakehouse architectures using Databricks, Spark, PySpark, and Delta Lake. The role covers ETL/ELT, data governance, security, quality, performance optimization, cloud integration, and CI/CD. It involves technical leadership across customer programmes, architectural decision-making, stakeholder collaboration, mentoring, and supporting reliable production data solutions.

Build and maintain reports, dashboards, and data visualizations using Power BI or Tableau. Extract and analyze data from SQL databases and Excel files, explain findings to teams and clients, troubleshoot reporting issues, and automate repetitive tasks. The role requires strong SQL skills, BI tool expertise, and conversational English for remote collaboration with global clients across varied industries.

Lead consulting engagements for defence, government, national security, and regulated-sector clients. Responsibilities include discovery, stakeholder workshops, digital and data strategy, target operating models, architecture, transformation roadmaps, AI and analytics opportunities, data governance, client deliverables, and proposal support. The role requires translating complex business and technical challenges into practical recommendations and measurable outcomes while influencing senior stakeholders and collaborating with multidisciplinary teams.

Lead and mentor a data science team building ML and LLM solutions for cybersecurity. Translate business needs into research and production ML objectives, prioritize roadmaps, collaborate cross-functionally, and drive technical vision, model development, and operationalization for malware detection and threat analysis.

Leads the enterprise data governance strategy, framework, roadmap, policies, standards, and data quality programs. Establishes metadata, lineage, master data, classification, access, and privacy practices while partnering with IT and Security to support secure AI initiatives. The role resolves cross-functional data issues, aligns governance with regulatory requirements, measures data quality, and develops the governance team in a regulated healthcare or technology environment.

Develop and optimize machine learning and generative AI solutions for digital agents, including LLMs, NLP, RAG, retrieval, experimentation, evaluation, and statistical analysis. Build scalable data and ML pipelines using Python, SQL, and Spark; assess model quality, reliability, safety, and business impact; and support production deployment. Collaborate with engineering, product, and data science teams while mentoring less experienced colleagues.

Create and present short-form football content as the face of Compare the Market’s EFL partnership. Attend EFL matches nationwide, engage fans, manage community interaction, and showcase club traditions through a recurring social media series. The role requires regular travel, confident on-camera presenting, strong editorial judgment, brand alignment, and the ability to work within approval processes while maintaining an authentic voice.