Top Data Engineer Jobs in Glasgow

Lead design and delivery of end-to-end data and AI solutions on Azure and Microsoft Fabric. Translate business needs into secure scalable data architectures, build and optimise pipelines, create enterprise Power BI semantic models, integrate AI/ML and LLM capabilities, support pre-sales, ensure governance and compliance, and mentor junior staff.

Design, build and maintain scalable data pipelines and Airflow orchestration; deliver Databricks-based data engineering solutions; collaborate via GitHub, support data science workflows, and promote observable, maintainable production delivery.

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.

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.

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.

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

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.

Design, build, and maintain scalable data pipelines and platform services to support analytics, ML, and product teams. Implement infrastructure-as-code (Terraform) and Kubernetes-based deployments, develop data services/APIs, and ensure data quality, observability, security, and compliance. Partner with cross-functional teams, document systems, and continuously improve performance and reliability.

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.

Build, test, and document secure batch and real-time data pipelines for a government client; design logical and physical databases; extend platform capabilities; ensure data models align with architecture; perform data analysis and root-cause investigations to improve processing and availability for downstream consumers.

Develop stored procedures or notebooks to build 230 additional data items into DCoE tables/views. Create an extensible, config-driven process to add fields with minimal re-engineering. Produce self-documenting, well-commented code and collaborate with the DCoE team on delivery; DCoE handles business analysis, design sign-offs, data modeling, and testing.

Design and deliver scalable Customer Data Platform solutions (Oracle Unity), build and operate data pipelines and Customer 360 models, integrate CDP with data lake/CRM/marketing systems, lead architecture and data modelling workshops, ensure data quality, governance, security, and support CDP operations, and partner with Oracle implementation teams.

Lead and mentor a Data Engineering team to design, build, and optimize scalable Microsoft-based data platforms (Fabric, Databricks, Synapse, Azure). Develop PySpark/Spark SQL pipelines, orchestrate with ADF/Synapse/Fabric Pipelines, manage Azure DevOps and CI/CD, scope tasks, review code, and drive Agile delivery while acting as technical SME and stakeholder partner.

Design, build, and maintain scalable data pipelines and backend data-processing applications using Java and SQL. Optimize data models, query performance, and data platform reliability; implement automated testing, monitoring, and data quality controls; collaborate with stakeholders to translate requirements into scalable ETL/ELT and near-real-time solutions.

Implement, configure and debug web and mobile tracking and MarTech systems (Tealium, GA4, MMPs). Ensure data quality, privacy/compliance, QA and documentation while partnering with MarTech, CRM and Product teams.

As a Senior Data Engineer, you will design and develop large-scale data processing software, manage technical components, and mentor team members, while ensuring code quality and operational readiness.

Design, build, and maintain scalable analytics models and automated reporting. Partner with Sales, Product, Marketing, and Finance to define KPIs, deliver ad hoc analysis, improve data quality and observability, and establish analytics engineering best practices to enable self-service analytics.

Design, build and optimise scalable cloud-native data platforms and pipelines using Microsoft Fabric, Azure Databricks, Synapse and ADF. Implement ETL/ELT with PySpark and SQL, ensure data governance, performance, reliability, and collaborate with data scientists and engineers to deliver production solutions.

Design, build, and maintain scalable data pipelines and Airflow orchestration; deliver Databricks-based data engineering solutions; support data science workflows; collaborate via GitHub, code reviews, and shared engineering standards; ensure reliable, observable, and maintainable pipelines.

Design, build, and maintain scalable cloud-based data platform and ETL pipelines on Azure. Develop data models, ensure data quality, optimize performance, implement governance and security, and collaborate with stakeholders and data scientists to enable data-driven solutions.

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.

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.

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.