Top Data Engineer Jobs in Edinburgh

The Cyber Security Data Operations Engineer will develop and manage data analytics applications, design solutions, and ensure effective documentation and user support for cyber security data processes.

Support design, development and maintenance of data product pipelines for regulatory security monitoring and compliance. Assist with data ingestion, telemetry, automation of control monitoring, and collaborate with engineers and security specialists while learning modern engineering practices.

Build the companys first data stack and data products (internal analytics and customer-facing). Work across data engineering, devops, and software tasks; ship early features, improve developer experience, document proposals, and iterate based on feedback.

Build and operate a new AI-focused data platform: design ingestion, transformation, semantic/ontology layers, storage, and DAG-based orchestration. Implement pipelines, select databases and orchestration tools, ensure performance, observability, and data quality, and collaborate with product and AI teams while remaining hands-on in coding and production operations.

Lead design and delivery of robust, scalable data pipelines and architectures in Azure Databricks to validate and transform policyholder data for actuarial modelling. Ensure data quality, governance, monitoring, and integration of multiple sources. Optimize performance, provide technical leadership to engineers/analysts, and transition the solution to BAU with audit-ready controls in a regulated financial services environment.

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.

The role involves developing data pipelines, managing metadata and data quality in Azure, ensuring compliance with data governance standards, and collaborating with data teams.

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.

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.

Build production AI-powered tools, data pipelines, and internal full-stack applications that automate incident investigation, surface anomalies, and enable AI agents to interact safely with platform systems. Work across Python, TypeScript, Go and SQL, deploy on Docker/Kubernetes/AWS, and collaborate with TechOps, Connectivity, and Data teams to own, monitor, and improve shipped systems.

As a Data Engineer, you will design, build, and maintain data pipelines and infrastructure to support data-driven decision-making in a collaborative environment.

The Data Engineer will build and manage scalable data pipelines and databases, collaborate on architecture, and drive data systems towards real-time capabilities in a fast-paced, innovative environment.

The Data Engineer will design and support data warehouses, create analytics dashboards, perform data quality analysis, and identify process improvements. Responsibilities include collaborating with stakeholders to develop insights and ensure data governance.

Design, develop, and maintain ELT/ETL Snowflake data warehouse solutions; implement data standards, CI/CD, monitoring, and production support; collaborate with product, business, and vendors; mentor junior developers and improve data engineering practices.

Client-facing Principal Data Engineer leading design and delivery of enterprise Databricks-based data platforms. Provide architecture leadership, mentor engineering teams, drive platform optimisation, define standards, support technical governance, and deliver across multiple consultancy engagements.

Support ledger migration and data lake cleanup by extracting, transforming, reconciling and validating large datasets. Develop and validate SQL queries, batch jobs and processing logic, perform regression testing, document mappings and validation rules, and collaborate with engineering, product and business teams to ensure production readiness and post-migration validation.

The Data Engineer will build and scale data pipelines, ensure data quality, implement dbt best practices, and empower cross-functional teams with self-service data access.

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 production data pipelines for a predictive CRM platform: monitoring, incident response, cost and performance optimization, migrating legacy ETL, onboarding customers, enforcing Delta Lake architecture and multi-tenant isolation, and applying CI/CD, security, and observability best practices to ensure reliable, scalable data delivery.

Design, build, and scale cloud-native data warehouses and robust ETL/ELT pipelines (primarily BigQuery). Optimize performance, implement partitioning and cost controls, ensure data governance, observability, and regulatory reporting support, and collaborate with analytics and engineering stakeholders.

Lead data integration, automation, and analytics for commercial teams: design data marts and architectures, build Airflow and Power Automate workflows, write SQL and Python pipelines, deliver BI reports (Power BI/Cognos), and run end-to-end data science projects to provide actionable business insights.

Design, implement and optimize scalable data solutions and pipelines on cloud platforms (Databricks/Azure). Support ETL/ELT, data modelling, data warehouse design, data quality, governance, and cross-functional stakeholders to enable analytics and data science at scale.

Triage and resolve Master Data Management (Party, Risk, Reference) issues in Stibo STEP, run targeted data-cleansing sprints, monitor data quality dashboards, support releases/UAT, and maintain documentation and playbooks to prevent recurring data issues.

The Senior Site Reliability Engineer will design and maintain a reliable data platform, develop data pipelines, automate processes, and ensure system observability while collaborating across teams.