Top Data Jobs in Edinburgh

Design, build and automate scalable data pipelines, ETL/ELT and streaming ingestion on AWS (S3, Glue) and Snowflake. Ensure data modelling, testing, cleansing, monitoring, security and cost-efficiency while partnering with stakeholders and the data engineering community to embed modern data practices and deliver customer-focused solutions.

Seeking a Data Engineer to build and maintain data pipelines and systems, ensure data quality, and collaborate with teams for analytics and business decisions.

Lead electrical design for data centre projects across all stages, define power architectures, run power systems and protection studies using ETAP/SKM, integrate BMS/EPMS/SCADA and metering, embed decarbonisation and energy-efficiency measures, ensure standards compliance, coordinate multidisciplinary teams, mentor staff, and support business development and project financial management.

Lead development and management of Data Transfer Specifications and standardization of external clinical data (e.g., eCOA, imaging, IRT). Ensure CDISC compliance, support data reconciliation and regulatory requirements, oversee external vendors, and collaborate with clinical study teams to maintain data quality and processes.

Senior leadership role to define and commercialise Data & Analytics capability, shape go-to-market for Data/AI, drive revenue and client growth, build and lead a multidisciplinary data organisation, ensure delivery excellence, governance and reusable assets across regulated sectors using Microsoft data technologies.

Join the Business Data team to manage and govern Business Banking data, deliver analytics and insights, design data strategy and integrations, implement controlled BAU processes, mitigate data risks, and explore responsible AI adoption to support Society and Business Banking objectives.

Lead the Business Data team within Business Banking to manage data governance, controls, strategy and analytics. Design and implement controlled, auditable BAU processes and automated data solutions, deliver insights, mitigate data risks, and explore responsible AI adoption while collaborating with stakeholders to support Society-wide business objectives.

The VP of Data Engineering is responsible for creating and scaling a data engineering capability, leading data pipeline design and delivery, and establishing best practices for data governance and AI-ready data ecosystems.

Build and maintain cloud-based data pipelines and analytical tooling for crypto market surveillance. Perform systematic and ad-hoc analysis to detect market abuse, support investigations, publish crypto disclosures, and enhance surveillance platforms using big-data, visualization, and blockchain analytics tools.

The Principal Product Specialist will drive sales strategy, manage Proof of Concept processes, and facilitate customer engagement for DSPM solutions across EMEA, ensuring product evolution based on customer feedback.

The Data Scientist will build and refine predictive credit models, analyze repayment data, and collaborate with teams to optimize credit strategies across African markets.

The Clinical Data Coordinator II assists in clinical trials by maintaining data management processes, handling data reconciliation, and resolving data issues, while collaborating with study teams.

Lead the data platform product to enable experimentation, production ML, AI-assisted analytics, data quality, automation for data engineering, scalable delivery, governance, and measurable business impact across product and data teams.

The Data Assistant will perform research and data collection on power plants, supporting the Power and Renewables team with data management and quality improvement.

The Data Architect/Data Engineer will design scalable data architectures, build and optimize data pipelines, ensure data governance, and support advanced analytics and personalization, particularly using Adobe Experience Platform.

Lead Evergreen Finance's data strategy, ensuring platform scalability, governance, and cultural adoption of analytics across teams. Guide technical and non-technical stakeholders in data-driven environments.

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.

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.

Deliver end-to-end data science projects: build and deploy ML/NLP and LLM-based solutions, design scalable data pipelines, integrate APIs and cloud platforms, implement RAG/vector search, create reusable tools, and collaborate with cross-functional teams to drive healthcare analytics products.

The Data Engineer will manage the end-to-end lifecycle of data pipelines, architect data solutions, liaise with clients, and ensure performance monitoring. Responsibilities include defining specifications for ETL processes, deploying Azure Data Factory pipelines, and collaborating with engineering teams on data integrity and reliability.

Lead a team of data engineers, overseeing data product development, guiding technical solutions, and collaborating with cross-functional teams to enhance data systems.

Lead the build of a greenfield data lake in Databricks, integrate source systems, and create an intelligence layer for business insights. Define product requirements, work closely with engineering, manage stakeholders, and develop GTM, commercial modelling, pricing, and value propositions. Support analytics/BI tools like Power BI and Sigma.

Design, build, and maintain scalable data pipelines and models (ingestion, modelling, orchestration). Ensure data quality via testing and monitoring, participate in code reviews, shape data architecture, and enable analytics/BI across the organisation.

Perform data collection, analysis, and interpretation to identify trends. Build and maintain dashboards and reports, ensure data accuracy through quality checks, collaborate with teams to deliver actionable insights, and support development of analysis methodologies.

As a Lead Data Scientist, you will manage complex AI and ML projects, leading from design to deployment while ensuring delivery meets business outcomes and compliance standards. You will provide technical leadership and mentoring to the team.