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.

Develop and maintain customer-facing analytical reporting, data pipelines, and data models. Build Python applications, ensure data quality and consistency, collaborate with analytics engineers, and enable business teams to access actionable data. The role also involves cloud data warehouse optimization, infrastructure as code, software engineering best practices, and cross-functional product collaboration within an agile SaaS environment.

Designs, builds, and maintains secure, scalable data pipelines and architectures using Python and Databricks. Develops data models, manages database backup and recovery strategies, resolves data quality issues, evaluates access controls, and optimizes workflow performance. Collaborates with cross-functional agile teams to deliver reliable enterprise data solutions and support analytics, reporting, security, and operational resilience.

Build and maintain reliable data pipelines using Python and SQL. Integrate REST APIs, databases, files, cloud storage, and third-party platforms; process and load data into warehouses for analytics and BI. Implement incremental and fault-tolerant loading, logging, monitoring, retries, error handling, and data quality checks. Work with ETL/ELT, Git, and potentially dbt, Spark, orchestration tools, cloud platforms, Docker, dimensional modeling, and BI datasets.

Own and expand third-party billing and usage integrations for a cloud cost platform. Build and maintain ingestion pipelines, normalize vendor data, manage orchestration and backfills, and ensure financial-grade correctness through reconciliation, deduplication, and data quality checks. Use AI throughout the engineering workflow, collaborate with product and support teams, and improve integration scalability. The role requires strong SQL and Python, cloud data warehouse experience, REST API integration expertise, and production pipeline ownership.

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.

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.

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 and maintain automated cloud data solutions, scalable ETL pipelines, and data warehouses. Own complex backlog tasks, promote code quality through reviews and pair programming, and use experimentation to improve solutions. Support analytical tools that deliver actionable insights, operational efficiencies, and business performance metrics. Requires strong experience with Azure or other cloud data technologies, data warehousing, ETL, Databricks, SQL, Python, and Spark.

Build and maintain automated cloud-based data solutions and scalable ETL pipelines. Design end-to-end data architectures, use Databricks, SQL, Python, and Spark, and ensure reliable, high-quality data delivery. Own complex backlog tasks, support code quality through reviews and pair programming, and experiment with innovative solutions. Help develop analytical tools that enable data-driven decisions, operational efficiency, and improved business performance.

Build and own scalable data systems from ingestion through data products. Design pipelines, transformation layers, data models, testing and quality frameworks, and CI/CD practices. Collaborate with analytics, data science, and product engineering teams, while monitoring reliability and optimizing infrastructure. Support team development through code reviews, technical pairing, documentation, and knowledge sharing.

Design, build, and own scalable data pipelines from ingestion through analytics and incentive payouts. Lead data platform architecture, including Snowflake, dbt, Iceberg, and S3 workflows. Establish data quality, lineage, observability, performance, cost, and SLA standards. Partner with engineering and analysts to support telematics accuracy and driver incentive reporting. Develop maintainable data models and transformation layers while using AI coding tools to improve development speed and quality.

Build and evolve CluePoints’ enterprise data platform using Microsoft Fabric and Azure. Responsibilities include migrating Azure SQL and ADF workloads, integrating SaaS and web data, developing scalable ingestion and transformation pipelines, maintaining Kimball dimensional models, and establishing CI/CD, data quality, lineage, security, and monitoring. The role partners with analytics leadership and supports trusted Power BI reporting across multiple business functions.

Own and develop the company’s Power BI reporting environment, including semantic models, dashboards, advanced DAX, Power Query transformations, governance, deployment, and performance optimization. Collaborate with Data Engineering and business stakeholders to define KPIs, validate data, improve reporting quality, and deliver scalable BI solutions. Support data architecture, data quality, ad hoc analysis, and continuous improvement across enterprise reporting assets.

Lead the architecture and scaling of Overstory’s data platform for AI-powered vegetation analysis. Design and operate large-scale geospatial and temporal data pipelines, orchestration systems, event-driven workflows, service-oriented architecture, data quality frameworks, and observability practices. Drive architectural standards, system design, scalability, reliability, and traceability while mentoring engineers and collaborating with data, ML, and product teams.

Lead end-to-end ETL design, development, validation, and production support for a banking Risk Data Warehouse. Use IBM DataStage and Oracle to deliver regulatory and credit risk data solutions supporting Basel 3.1, Murex migration, and finance reporting. Own data lineage, controls, governance, monitoring, runbooks, incident escalation, root-cause analysis, and corrective actions while collaborating with risk, trading, and engineering stakeholders.

Designs and delivers scalable data pipelines and Lakehouse architectures using Databricks, Spark, PySpark, and Delta Lake. The role covers ETL/ELT, data governance, security, quality, performance optimization, cloud integration, and CI/CD. It involves technical leadership across customer programmes, architectural decision-making, stakeholder collaboration, mentoring, and supporting reliable production data solutions.

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.

Develop and maintain enterprise Power BI and Tableau reporting solutions, data pipelines, models, dashboards, and analytics using Snowflake, SQL, dbt, and Alteryx. Analyze institutional and external data to support strategic planning, forecasting, recruitment targets, and executive decision-making. Ensure data quality, reporting governance, release standards, and stakeholder satisfaction. Provide technical guidance, training, and support across the University, contribute to emerging AI-enabled analytics, and potentially line-manage junior developers.

Lead the technical direction and architecture for Arbor’s data platform, spanning transactional, analytical, and AI systems. Define target architectures, guide technical leads, support product delivery, and enable AI-first engineering through LLM orchestration, RAG, and real-time inference infrastructure. The role includes mentoring, architectural reviews, data governance, security, compliance, observability, DataOps, cloud platform cost optimization, and cross-functional collaboration with product and commercial leaders.

Own the end-to-end design, reliability, resilience, and recovery of production data ingestion systems and pipelines. Build batch and streaming ETL/ELT workflows, establish data quality checks and SLAs, improve monitoring and alerting, resolve platform issues, reduce technical debt, and optimize performance and cost. Collaborate with stakeholders, AI teams, and international expansion initiatives while supporting time-series, textual, and event-stream data workloads.

Designs, builds, and maintains scalable data pipelines supporting data science and advanced analytics. Manages Airflow orchestration and delivers data engineering solutions using Databricks. Collaborates through GitHub, code reviews, and shared engineering standards in feature-led environments. Partners with data scientists, owns delivery outcomes, performs root-cause analysis, and continuously improves pipeline reliability, observability, and maintainability. Exposure to MLOps concepts and tooling is desirable.