Top Data Jobs in Edinburgh

Lead design and governance of customer data solutions for retail: identity resolution, MDM, integration, and modern data platforms (lakes/lakehouses). Create design artefacts, enforce design principles, review solutions for compliance, collaborate with stakeholders, and apply industry trends to improve personalization and analytics.

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 design, build, and operate high-quality cloud software for the Customer Data Platform. Own technical delivery end-to-end, mentor engineers, support on-call, improve observability, testing, security, and collaborate with Product, Design and adjacent teams to deliver scalable solutions.

Lead design, build, test, and operate cloud-based services for the Customer Data Platform. Deliver complex features end-to-end, improve code quality, support on-call operations, mentor engineers, collaborate with Product/Design, and drive reliability, observability, and secure engineering practices.

The Senior Product & Growth Data Scientist will analyze user behavior, run experiments, shape product strategy, and influence growth metrics to drive company success.

As a Data Scientist, you'll empower the company with data insights, develop metrics and experiments, and collaborate with various teams to drive product decisions.

Lead and grow Modash's Data Products focusing on API customer journey, revenue growth, and team management, aiming for significant revenue increase.

The Senior Data Engineer will design and maintain scalable data pipelines, ensure data quality, and develop backend services to support WalletConnect's payments infrastructure and analytics.

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.

Design and implement core YTsaurus functionality in C++, build and evolve platform-level capabilities, improve APIs and UX for internal users, own production quality including incident response/on-call, and collaborate with stakeholders to prioritize and deliver scalable data platform features.

Lead high-complexity analytics projects: partner with stakeholders, prepare and blend large datasets, apply intermediate statistics, build models and visualizations, deliver insights, and mentor junior analysts.

The Data Scientist will analyze data, build and refine machine learning models, document technical designs, and collaborate with multidisciplinary teams to deliver AI solutions.

The Data Standards Lead will develop and govern data standards for licensing, ensure data interoperability, and guide teams in applying these standards.

As a Data Engineer, you will develop and manage data pipelines, ensuring reliable data ingestion and delivery while optimizing for simplicity and performance.

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.

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.

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 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 role involves programming and data review for real-world data analysis, supporting epidemiologists, and managing complex data sets using statistical programming languages and tools.

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.

Build and maintain scalable data pipelines and platform components to serve analytics, ML, and product teams. Design data services/APIs, ensure data quality, observability, security and compliance, and evolve cloud-native infrastructure using IaC and Kubernetes. Partner with cross-functional teams, produce documentation, and ship reliable, tested data solutions.

This role involves owning the EMEA pipeline, executing GTM plans, driving conversion, and improving lead quality across various channels and markets.

Perform full-stack on-chain and product analytics: query on-chain data, profile ecosystem actors, benchmark protocols, define KPIs, build dashboards, and translate findings into actionable briefs for strategy, BD, product, and growth teams.

Design, build, test and document secure batch and real-time data pipelines, perform logical/physical database design and data modelling, expand platform capabilities, analyse and resolve data processing issues, and ensure alignment with data architecture for downstream consumers.