Top Data Jobs in Edinburgh

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.

Supports Iron Mountain’s data center operations within its broader information management and digital services business. The posting provides limited details, with responsibilities and qualifications marked as “TBC.”

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.

Serve as the technical authority for enterprise customers in data security, owning the technical sales process. Deliver technical presentations, demos, and whiteboard sessions, advise on AI/ML-driven data protection, and drive outcomes through value-based selling with partners and GTM teams.

Leads North American indirect tax requirements and global tax data governance for an SAP S/4HANA transformation. Oversees U.S. and Canadian tax processes, tax determination, reporting, reconciliations, data standards, tax technology integrations, testing, deployment, audit readiness, and compliance. Partners across Tax, Finance, IT, Master Data, Analytics, Procurement, Supply Chain, and Shared Services to improve tax data quality and establish scalable global operating models.

Drive sales growth for Fluke’s data centre solutions across the UK, Ireland, and Nordics. Responsibilities include territory planning, prospecting, pipeline generation, forecasting, distributor and partner management, customer relationship development, product demonstrations, training, proposals, negotiations, CRM management, and industry events. The role requires technical understanding of data centre applications, consultative selling skills, and frequent engagement with end users, integrators, consultants, and channel partners.

Drive end-to-end B2B sales of Data and AI consulting services in the US market. Responsibilities include prospecting, pipeline generation, solution selling, account planning, business-case development, executive stakeholder engagement, partner collaboration, and closing enterprise and mid-market opportunities involving Databricks, Lakehouse modernization, data engineering, analytics, AI/ML, and GenAI.

Own positioning, messaging, go-to-market strategy, launches, pricing, packaging, market intelligence, sales enablement, and copywriting for developer-facing technical products. Partner cross-functionally with Product, Sales, Growth, Developer Relations, and ecosystem teams to drive pipeline, revenue, adoption, and product roadmap insights. The role also supports AI-agent discoverability and requires hands-on technical fluency, analytical skills, and strong English writing.

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.

The Data Scientist (Product Analytics) will develop product analytics, improve data quality, conduct A/B tests, analyze user behavior, and collaborate with cross-functional teams to inform product strategies and enhance user engagement.

Lead a specialist BPA data team to manage end-to-end pension scheme data for buy-in/buy-out transitions. Ensure data accuracy, validation, reconciliations, benefit specification oversight, governance, regulatory compliance, stakeholder integration, third-party data management, and drive automation and continuous improvement to support successful scheme migrations.

Support implementation of data governance frameworks across Pensions & Savings. Work with Data Owners, Stewards, and Risk to monitor data quality, track governance actions, maintain documentation, and produce regular and ad-hoc reports to support regulatory compliance and governance maturity. Contribute to continuous improvement of data governance processes and stakeholder reporting.

Leads data engineering teams building scalable data pipelines, lakehouse architectures, and data products on Databricks and AWS. Oversees hiring, coaching, delivery planning, architecture, platform reliability, SLAs, incident response, monitoring, and cost optimization. Partners cross-functionally with architects, product managers, analytics, and engineering teams to establish technical strategy, governance, and operational excellence.

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.

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.

Design and deliver production-grade AI/ML solutions for Kainos Workday products, including LLM-driven features, model deployment, dashboards, and mentoring junior staff. Collaborate with engineering and clients to improve product functionality, scalability, and efficiency.

Lead customer underwriting proof-of-concept projects using SQL, Python, and AWS to transform, validate, and deliver insurance risk data. Match address data to reference identifiers, investigate data-quality issues, document outputs, support underwriting data platforms, and automate manual workflows. Collaborate with technical and non-technical stakeholders, communicate project updates, and improve scalable data processes, quality, and customer experience.