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.

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.

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.

Designs and supports enterprise-scale data and document migration solutions across Customer Operations. Responsibilities include migrating data into Pega and documents into IBM CMOD, building ETL and integration pipelines, developing complex SQL, resolving data quality and reconciliation issues, analyzing metadata and lineage, and supporting cloud-based data platforms. The role requires collaboration with engineers, architects, product owners, and business stakeholders to modernize legacy systems and deliver secure, scalable data solutions.

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.

Designs, develops, and maintains scalable data structures, models, and automated pipelines for analytical and reporting needs. Integrates multiple source systems, transforms data into reusable datasets, monitors data quality, and implements validation and reconciliation processes. Collaborates with analysts and stakeholders to translate reporting requirements into datasets, maintains data documentation and lineage, and improves automation and efficiency. This is a UK-based remote role with occasional onsite attendance.

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.

Build and scale governed data products, pipelines, analytical models, semantic layers, APIs, and AI-enabled interfaces. Establish standards for data modeling, orchestration, testing, observability, CI/CD, security, and lifecycle management. Partner with stakeholders to define data contracts and product roadmaps, deliver solutions end to end, improve analytics governance, and mentor colleagues. The role requires strong data and analytics engineering expertise, product judgment, and practical experience directing AI coding agents.

Design, develop, and support scalable cloud-based data solutions across AWS and Azure. Build ETL/ELT pipelines, data integrations, migration processes, warehouses, data lakes, and lakehouses. Perform data profiling, quality assessments, validation, reconciliation, troubleshooting, and root-cause analysis. Create source-to-target mappings and technical documentation while supporting data governance, security, and compliance. Collaborate with technical and business stakeholders to deliver secure, high-quality data solutions.

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.

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.

Lead the design, deployment, and operation of scalable data pipelines and cloud infrastructure for Kpler’s marine insurance initiative. Build containerized AWS services using Python, Docker, Kubernetes, Airflow, and Terraform; manage PostgreSQL and Kafka/Flink streaming systems; establish observability standards; provide technical direction; collaborate cross-functionally; and mentor engineers.

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.

Own the company’s end-to-end data platform, including ingestion, S3 data lakes, ClickHouse, dbt transformations, orchestration, and BI/CRM data delivery. Improve reliability, historical accuracy, cost, and metric governance while defining and defending business KPIs. Partner with Customer Success, Sales, Product, and leadership to translate business questions into durable metrics and establish data standards. This is a senior individual contributor role focused on hands-on engineering, analytical rigor, and stakeholder communication.

Build and launch scalable data pipelines, products, APIs, and tools for proprietary market research. Develop big data processing systems, reusable software components, AI-ready data assets, cloud infrastructure, and governance controls. Partner with investment professionals, researchers, data scientists, and Compliance to deliver reliable, auditable, compliant solutions while optimizing system performance, cost, and reliability.

Leads the design, development, testing, integration, and optimization of scalable data-processing and persistence components. Establishes engineering and operational standards, ensures pipeline performance and compliance, troubleshoots systems, supports proposals, and collaborates with architects, operations, security teams, customers, and managers. Coaches and develops engineers while overseeing code quality, data models, testing, deployment readiness, and technical delivery.

Designs, develops, and supports secure cloud-based data integration solutions using Microsoft Fabric and Azure. Responsibilities include client discovery, requirements gathering, ETL and ELT development, REST API and SFTP integrations, data pipeline implementation, production monitoring, incident investigation, and reliability improvements. The role collaborates with architects, analysts, project managers, and delivery teams across multiple client engagements.

Build and operate scalable cloud data pipelines, warehouses, transformation layers, and ML production systems. Lead data infrastructure architecture, warehouse performance optimization, DataOps monitoring, CI/CD improvements, data quality testing, and large-scale data product delivery. Collaborate across teams, promote data stewardship, and support batch and streaming pipelines while deploying, serving, and monitoring machine learning models.

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.

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.

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.

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.