Top Data Engineer Jobs in Glasgow

Build and maintain secure, scalable data pipelines, architectures, and analytics solutions supporting AI and risk initiatives. Responsibilities include data provisioning, governance, lineage, metadata management, data quality controls, platform security, AI-assisted development, rapid prototyping, and operational stability. The role collaborates with business, technology, and control stakeholders to improve data flows, reduce operational effort, and support data products, AI/ML deployment, and natural-language query capabilities.

Lead the technical direction and standards for Azure data engineering. Design and assure scalable, secure pipelines and lakehouse architectures, review production changes in Azure DevOps, conduct proof-of-concept development, and research new platform capabilities. Collaborate with data scientists, architects, developers, platform teams, support teams, and external partners to ensure reliable, governed data solutions and smooth transition into support. The role provides hands-on technical leadership without direct line management.

Lead data engineering for AI and analytics initiatives, building secure and scalable data pipelines, platforms, and tooling. Responsibilities include data profiling, quality remediation, lineage and metadata management, governance controls, analytics prototyping, AI-assisted development, and operational stability. The role partners with business, technology, risk, and executive stakeholders while driving data product adoption, AI readiness, regulatory traceability, and continuous improvement across enterprise data flows.

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.

Lead client-facing data platform architecture and strategy. Design scalable Databricks/Azure or AWS-based solutions, oversee multi-team delivery, incident response, governance, and data security. Mentor engineers, drive roadmap and budget decisions, and ensure resilient, high-quality data pipelines aligned to business goals.

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.

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.

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.

Design, build, and maintain scalable data pipelines and Microsoft Fabric artefacts; integrate data into Azure Data Lake/OneLake; perform ELT/ETL and advanced transformations using SQL, Python, and Spark; support Power BI models; apply Purview governance; troubleshoot production issues; participate in CI/CD and mentor junior engineers while engaging with clients and cross-functional teams.

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.

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

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.

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.

Build, test, optimize, and maintain ELT pipelines supporting analytics, reporting, and data products. Deploy config-driven workflows to Databricks across a multi-tenant environment, extend data models, support CI/CD and ingestion projects, monitor pipeline health, troubleshoot failures, and collaborate with data and product teams to deliver reliable, well-tested data.

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

Lead and architect enterprise-scale cloud data solutions, mentor data engineers, design and build batch and streaming pipelines using Databricks and Microsoft Fabric, implement Lakehouse/Medallion patterns, ensure data governance/security, and drive CI/CD and IaC practices across projects.

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.

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.

Design, build, and maintain high-volume distributed data processing jobs and pipelines (Redshift, Athena, Iceberg, Spark, Airflow). Collaborate with data scientists and analysts to productionize models, optimize performance, troubleshoot systems, and mentor junior engineers. Support lakehouse/data warehouse management and ensure data accuracy and efficiency.

Design, build, and maintain scalable batch and streaming data pipelines, internal tools, and APIs to support analytics, product features, and ML. Collaborate with product and engineering to improve data infrastructure, ensure reliability, testing, and observability in a cloud-native environment.

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.

Lead the discovery and analysis of a large, undocumented SQL Server estate supporting financial services trading data. Responsibilities include mapping schemas, dependencies, stored procedures, data lineage, integrations, duplicated tables, and potential dead code; developing archiving and retention strategies; documenting findings; advising on modernization and cloud migration; and mentoring data engineers. The role requires hands-on SQL Server and AWS RDS expertise, scripting with Python or PowerShell, AI-assisted analysis using Claude, and client engagement in a hybrid Glasgow consultancy setting.

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.

Design and maintain reliable data solutions, lead a squad of engineers, develop scalable data pipelines, and ensure adherence to data standards.

Design, deploy, and maintain AWS and Databricks data infrastructure, data lakes, and scalable ETL pipelines. Build custom ingestion services and connectors using Airbyte, Go, and Python; optimize performance, reliability, monitoring, and data quality. Implement cloud data governance, security, access controls, encryption, and compliance practices while collaborating with platform engineers, analysts, and stakeholders.