Top Data Jobs in Edinburgh

Manage requests for new ServiceNow reports and dashboards, resolve dashboard/reporting incidents, train users on dashboards, build executive KPIs and scorecards, engage stakeholders to gather requirements, and apply AI/Generative AI for reporting automation and insights.

Own cloud architecture intelligence and cost optimization: analyze cloud spend, identify inefficiencies, build dashboards, enforce governance, partner with engineering/finance to drive cost-conscious design and operations.

The Forward-Deployed Data Analyst will engage with African Ministries of Health, build data management solutions, develop dashboards, and support public health decision-making.

Lead Kraken's Data Platform team focused on real-time infrastructure for data systems, managing team dynamics, architecture design, and promoting AI automation.

The Data Product Analyst designs and manages data products for insurance domains, ensuring they meet analytical and compliance standards while collaborating with cross-functional teams.

As a Lead Data Scientist, you will set technical direction, ensure project delivery, manage client relationships, and mentor data scientists.

Lead the development of backend services for government clients, focusing on high availability and scalability, while collaborating in agile teams and maintaining deployment pipelines.

The Lead Data Architect will shape data strategy, design secure data architectures on AWS, mentor teams, and ensure compliance with regulations.

As a Curriculum Manager, you will manage the content development lifecycle, collaborate with instructors, and improve data science curriculum, ensuring technical accuracy and pedagogical effectiveness.

Lead delivery of advanced AI solutions, provide technical and architectural leadership, drive adoption of cloud-native AI engineering, and manage, coach, and develop a multidisciplinary data science team while ensuring responsible AI practices and strong stakeholder engagement.

Lead design, development, and operation of real-time and batch data pipelines and cloud infrastructure. Improve reliability, observability, and data architecture; own features end-to-end and mentor engineering teams while coordinating cross-team stakeholders.

Entry-level data engineer to follow a structured 12-month development programme. Build internal reporting, reusable engineering frameworks, documentation, and tooling while learning SQL, Python, Databricks and cloud data services. Support senior engineers, join code reviews, shadow customer workshops, and progressively contribute to client projects.

Design, build and maintain cloud-native ETL/ELT pipelines and data lake solutions using dbt, Snowflake/Databricks and AWS services. Optimize SQL and data models for performance and cost, create dashboards, follow CI/CD and software engineering best practices, and collaborate with architects, engineers and clients to deliver production-ready enterprise data solutions.

Design, deploy, and govern enterprise-scale data architectures and platforms to support analytics and AI. Define modeling standards, integration pipelines, data quality, lineage, and security. Lead technical direction, mentor engineers, evaluate AI tooling, and support proofs of concept and solution roadmaps that enable generative AI and RAG solutions.

Build, optimise and stabilise AWS-based data pipelines (S3, Glue, Redshift, Lake Formation). Resolve technical debt, support the Architect on target-state design, collaborate with client engineering teams to transfer knowledge, and deliver production-ready platforms in regulated, high-volume gaming environments.

Own and operate Databricks-based data platform production support, build and migrate ETL/ELT pipelines to Databricks across Azure/AWS, optimize compute and performance, enforce Delta Lake architecture and data security, implement observability and CI/CD, support multi-tenant onboarding and on-call incident response, and maintain runbooks and SLA metrics.

The Software Engineer will provide technical support to customers, collaborate with engineering teams, and drive customer success initiatives while leveraging Python and SQL expertise.

As a Senior Data Engineer, you will manage complex data projects, lead small teams, and work with clients on data engineering solutions. Responsibilities include ETL development, data pipeline orchestration, and customer relationship management.

Lead Oyster's data platform and AI strategy, centralize AI initiatives into production systems, build scalable data and MLOps infrastructure, modernize knowledge architecture and semantic search, partner cross-functionally to embed AI into products and workflows, enforce tooling standards and governance, and drive organizational adoption and change management for AI-assisted operations.

The Curriculum Manager will oversee the content development lifecycle, recruit instructors, design curriculum, assess course performance, and improve data science and engineering content.

Lead project teams in delivering bespoke algorithms, mentor other data scientists, and shape client relationships in the defence and national security sector.

Embed with customer teams to build production-ready data and AI solutions. Own full lifecycle from data acquisition and preparation through modeling, evaluation, and deployment. Drive model evaluation tied to business outcomes, ensure ethics/privacy in data use, coach cross-disciplinary teams, research industry trends, and deliver scalable operational models that influence strategy and customer outcomes.

Customer-embedded Principal Data Scientist who leads account-aligned delivery: translate business needs into technical approaches, build and ship production-grade solutions, influence senior stakeholders, prepare transitions to FDE crews, and operate with speed and entrepreneurial ownership to drive sustained business outcomes.

Lead discovery, design and delivery of data modernisation and migration projects. Design data lakes/warehouses/lakehouses and landing zones, advise on data strategy, governance and quality, implement Microsoft/Azure and Databricks platforms, support AI/ML initiatives, run workshops, produce technical documentation, contribute reusable assets, and support pre-sales and practice development.

Design, build and maintain scalable, automated cloud data solutions and pipelines. Provide technical leadership to a small engineering squad, own complex backlog tasks, develop data frameworks and standards, and ensure code quality via reviews and pair programming using Databricks, Python, SQL and Spark.