Top Data Jobs in Edinburgh

Leads Swiss-AS AMOS configuration, aircraft data migration, validation, testing, and post-implementation support for aircraft induction and fleet transition projects. Manages data extraction, cleansing, mapping, transformation, reconciliation, reporting, and audit documentation. Coordinates with Engineering, CAMO, Maintenance Planning, Supply Chain, Quality, IT, OEMs, and lessors to ensure data integrity, regulatory compliance, and operational readiness. Supports project cutover, go-live, user training, and regulatory audits.

Lead MEP cost and commercial management for large data centre projects in the Nordics. Manage teams, deliver cost reporting, assure contract compliance, develop cost systems, build client relationships, and drive continuous improvement and innovation.

Lead and manage FIA and OFE delivery across multiple data centre projects. Act as Owner's Representative/PMC to coordinate vendors, validate schedules (TTS/TTD), oversee FAT and site sign-off, manage risks, scope and design changes, and ensure contractor interface and logistics for equipment commissioning and deployment.

Lead cost and commercial management for a major Nordic data centre project, creating budgets, managing finances and teams, ensuring contract compliance, delivering commercial analysis, building senior client relationships, and improving cost management processes.

Lead design and delivery of scalable analytics, BI, forecasting, and AI-driven solutions for RevOps Sales Intelligence. Partner cross-functionally to improve data quality, governance, automation, and self-service analytics using cloud data platforms and modern ML/Generative AI techniques. Manage roadmap, stakeholder communication, and operationalize AI/analytics at enterprise scale.

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.

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.

Lead the development of Smart Data Products within Actian's ecosystem, managing all stages of the product lifecycle and collaborating with cross-functional teams to drive innovation and adoption.

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

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

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.

Build and maintain production data pipelines and features in Python; own project lifecycle from design to reliable production; set technical direction for the Insights platform; improve engineering standards (testing, observability, data quality); apply AI across products and pipelines; help establish semantic/AI-ready data layers.

Lead UK sales strategy for data center customers, develop strategic relationships with hyperscalers and colocation providers, manage pipeline and forecasting, coordinate cross-functional pursuit teams, and drive profitable growth through customer engagement, specification, negotiation and long-term account development.

Lead end-to-end SAP S/4HANA data migration activities: define strategy and governance, manage mapping, cleansing, ETL and reconciliation, execute mock and production loads, resolve defects, coordinate with testing and business teams, and support cutover readiness.

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.

Leads the Payments data strategy and partners with Product, Engineering, and Design to shape payment products, commercial optimization, customer growth, metrics, architecture, and operating models. Builds and scales a high-performing, full-stack data organization spanning data science, analytics engineering, machine learning engineering, and analytics. Serves as a trusted Payments Leadership partner, translating technical, product, and commercial perspectives into high-impact decisions.

Build production-grade AI agents, integrations, data pipelines, and infrastructure directly within strategic customer environments. Engineers rapidly navigate unfamiliar codebases and data stacks, own technical delivery through production deployment and handoff, communicate credibly with customer engineering teams, document and train users, and feed recurring engagement insights back to Product and Engineering. The role requires 30–50% travel during active engagements and emphasizes backend systems, agent integrations, real-world deployments, and maintainable customer outcomes.

Build, test, optimize, and maintain ELT pipelines supporting analytics, reporting, and data products. Deploy config-driven workflows to Databricks across a multi-tenant environment, extend data models, support CI/CD and ingestion projects, monitor pipeline health, troubleshoot failures, and collaborate with data and product teams to deliver reliable, well-tested data.

Develop and deploy machine learning and statistical models that solve business problems and create measurable impact. Responsibilities include exploratory data analysis, feature engineering, experimentation, model optimization, explainability, MLOps, prompt engineering, and cloud-based data collaboration. The role involves working with clients and stakeholders, communicating insights through visualization and storytelling, and contributing to scalable analytics, Generative AI, and data science solutions.

Lead enterprise-scale data science and AI initiatives, defining lifecycle best practices, architecture, governance, metadata frameworks, and MLOps practices. Design and deploy machine learning, statistical, optimization, and LLM solutions using cloud platforms. Lead multidisciplinary teams, translate business needs into analytical solutions, collaborate with engineers and stakeholders, and communicate insights to technical and executive audiences.

Coordinate group payroll processes across UK and rest-of-world, liaise with Global Mobility, P&C and outsourced providers, handle payroll queries and calculations, prepare journals for Oracle, update payroll procedures, and support meetings and implementation.

Lead high-complexity data science initiatives improving search ranking, retrieval, relevance, and evaluation. Develop and deploy production machine-learning and NLP solutions, investigate LLM-based ranking features, optimize SQL queries, and work with sparse privacy-preserving data. Define strategies for ambiguous problems, guide other data scientists, collaborate with product teams, and own projects from scoping through production.

Lead and grow a data engineering team; design and deliver scalable data pipelines, platforms, and data products; mentor engineers; collaborate with product, data science, and business stakeholders; ensure data quality, governance, reliability, and continuous improvement.

Design, build, and maintain scalable batch and streaming data pipelines. Write and review code (Python, SQL, Spark), implement data quality checks, monitor pipelines, and collaborate with analysts, data scientists, and product managers to deliver reliable, documented data solutions.