Top Data Jobs in Bristol

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 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.

Owns customer-growth analytics across onboarding, activation, trial-to-paid, multi-product adoption, retention, and expansion. The role develops segment-level behavioral and revenue insights, causal-inference and experimentation standards, canonical metrics, and value frameworks. It advises Growth, GTM, Finance, and product leaders, influences investment decisions, identifies data gaps, and sets technical direction for reusable growth-measurement methods across teams.

Designs and optimizes scalable data pipelines and architectures supporting analytics and operational needs. The role develops ETL, streaming, data quality, governance, and cloud-based workflows using modern big data technologies. Responsibilities include translating business requirements into technical solutions, monitoring data systems, improving reliability and performance, and providing technical guidance on data modeling, storage, and metadata management.

Arcadis is building a talent network for experienced data centre project professionals for future European assignments. Potential roles include Project Director, CSA, electrical, or mechanical Project Manager, and Construction Manager. Responsibilities span multidisciplinary leadership, construction and infrastructure delivery, contractor coordination, programme, cost, safety, quality, commissioning, stakeholder management, and client relationships across hyperscale and mission-critical projects. Rotational assignments across Europe may be required.

Leads a Data Products team designing, developing, and evolving foundational data products for energy-market modeling, analytics, and client-facing systems. Owns data product quality, reliability, governance, schemas, versioning, metadata, and data contracts. Provides technical leadership, collaborates with subject matter experts and platform engineers, establishes engineering best practices, and supports agile delivery and continuous improvement.

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.

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.

Leads client engagements delivering data, analytics, automation, and AI solutions for complex programmes and business operations. Responsibilities include defining requirements, designing data processes and pipelines, resolving data quality issues, providing technical delivery assurance, applying generative AI and LLM solutions, managing stakeholders, supporting proposals, and mentoring teams. The role also contributes to reusable methods, documentation, innovation, and client relationship development.

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 statistical and machine learning solutions for customer and energy-transition challenges, from problem discovery and experimentation through prototype validation and production handover. Lead end-to-end analytical workstreams, collaborate with customers, engineers, domain experts, and product teams, and mentor data scientists. Build models for time-series and text data using Python and data science libraries, working with databases, APIs, pipelines, Kubernetes, Linux, Git, and Docker.

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.

Owns the integration and analysis of people, security, technology, fraud, physical security, investigation, and governance data to identify insider-risk patterns. The role produces actionable intelligence, supports investigations, strengthens controls, and develops monitoring, automation, governance, and reporting capabilities. It requires influencing senior stakeholders, translating complex findings into business outcomes, and making risk-based recommendations amid ambiguity.

Delivers data governance initiatives by gathering requirements, documenting processes and data flows, supporting Microsoft Purview adoption, and improving data quality. Works with data owners, stewards, technical teams, and senior stakeholders to define governance processes, metadata standards, business glossaries, ownership models, controls, and quality metrics. Facilitates workshops, translates business needs into requirements and user stories, investigates data issues using SQL and profiling outputs, and communicates risks, dependencies, and decisions.

Manages virtual data rooms for client transactions and internal projects, including setup, permissions, document upload, indexing, quality checks, redaction, user support, reporting, closure, archiving, and audit logs. Ensures secure handling of confidential information and regulatory compliance while supporting legal teams, clients, and advisors. Contributes to improving data room processes and tools.

Analyze transactional data to detect and prevent financial crime, manage AML alerts, build SQL-based models and dashboards, and improve transaction monitoring and customer risk-rating controls. The role owns financial crime operations capacity planning, models scenarios, identifies data-led improvements, and presents insights to business leaders while collaborating with Product, Compliance, and cross-functional stakeholders.

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.

Performs data-heavy functional, regression, and User Acceptance Testing for Finance change initiatives. Designs test cases from requirements and acceptance criteria, validates data using complex SQL and Excel, logs and tracks defects, supports issue resolution, and reports testing progress. Works closely with subject matter experts, business users, and delivery teams in a regulated banking environment.

Build and maintain automated cloud data solutions, scalable ETL pipelines, and data warehouses. Own complex backlog tasks, promote code quality through reviews and pair programming, and use experimentation to improve solutions. Support analytical tools that deliver actionable insights, operational efficiencies, and business performance metrics. Requires strong experience with Azure or other cloud data technologies, data warehousing, ETL, Databricks, SQL, Python, and Spark.