Top Data Engineer Jobs in Glasgow

Design, build, and maintain secure, scalable data pipelines and architectures using Python and Databricks. Develop data models, support analytics and reporting, resolve data quality issues, and implement database backup, recovery, and archiving strategies. Evaluate data access controls, optimize pipeline performance, and collaborate with cross-functional teams to deliver reliable enterprise data solutions.

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, develops, tests, and supports reliable data engineering solutions for insurance data products. Responsibilities include data transfers, profiling, validation, quality management, issue resolution, data modeling, workflow management, code reviews, troubleshooting, and automation. The role collaborates with technical teams and business stakeholders to translate requirements into scalable, maintainable solutions while exploring AI-assisted engineering practices.

Own EULER’s data lifecycle, including instrumentation, validation, reconciliation, privacy, anonymization, canonical data modeling, analytics datasets, documentation, and production pipelines. The role requires strong SQL, software engineering practices, independent ownership, skepticism toward data quality, and clear communication. Preferred experience includes GDPR/LGPD, multi-tenant B2B SaaS, dbt, statistics, Cloudflare Workers, Postgres, Parquet, DuckDB, and Bubble.

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 technical direction of data engineering across client engagements. Design migration frameworks, streaming architectures, Lakehouse platforms, data integrity systems, and regulatory reporting pipelines. Provide architectural authority, review technical designs, select cloud services, mentor senior engineers, develop reusable delivery patterns, support hiring, and represent the consultancy externally. Shape proposals, scope technical engagements, present to senior stakeholders, and identify opportunities with existing clients.

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 maintain FinOps data pipelines, BigQuery and dbt models, cost attribution systems, forecasting and anomaly detection models, and cost-versus-revenue analytics. Identify and implement AWS and GCP savings opportunities, automate governance with Python, Terraform, and Lambda, and partner with Engineering, Finance, Product, and leadership on cloud costs, forecasting, reporting, and architecture decisions.

Develop and maintain data pipelines, models, ETL workflows, validation processes, and interfaces for defence modelling and simulation data. Transform complex simulation outputs into reliable datasets for experimentation, analysis, visualisation, and capability assessment. Collaborate with modelling and simulation engineers, software engineers, analysts, and domain specialists. The role requires Python, SQL, data-quality practices, and the ability to work in offline or restricted environments supporting UK Ministry of Defence projects.

Develop and maintain data pipelines, models, ETL workflows, and validation processes for defence modelling and simulation data. Integrate simulation and analytical environments, automate repeatable processing, and make complex datasets accessible for experimentation, analysis, and visualisation. The role requires Python, SQL, data modelling, and experience working in offline or restricted environments, supporting UK Ministry of Defence projects.

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.

Own and develop Azure Databricks data-platform workstreams across data modelling, medallion architecture, Delta tables, data quality, infrastructure, CI/CD, DevOps, security, and governance. The role requires autonomous delivery from scoping through production, troubleshooting Azure infrastructure, managing Terraform and Azure DevOps, protecting PII, and engaging technical and business stakeholders globally.

Own and modernize end-to-end financial data pipelines, predictive models, and cloud infrastructure. Build real-time market-data ingestion, migrate services to AWS, rewrite PHP calculations in Python, and manage time-series and search databases. Implement data quality, monitoring, alerting, containerized deployments, CI/CD, and GitOps workflows. Collaborate with quantitative researchers and analysts while mentoring engineers and guiding architecture.

Own and enhance data intelligence products, BI solutions, dashboards, KPI frameworks, data governance, master data management, and workflow automation. Translate business objectives into actionable insights, scalable architectures, roadmaps, and measurable commercial outcomes. Analyze travel and meetings data, improve data integrity, document systems and processes, support client implementations, and collaborate with Sales, Finance, Technology, and leadership teams.

Lead the technical direction of client data engagements, designing Microsoft Fabric and Azure data platforms, pipelines, models, testing, CI/CD, and operational practices. Manage and technically guide data engineers while remaining hands-on with complex challenges, incident response, and troubleshooting. Translate client requirements into technical roadmaps, collaborate with stakeholders, and ensure high-quality, commercially sound delivery using Python, PySpark, SQL, and modern data architecture patterns.

Designs, builds, and maintains production data pipelines using Microsoft Fabric, Python, and PySpark. Owns architecture, data modeling, CI/CD, reliability, troubleshooting, and technical decision-making. Collaborates with architects, product teams, and clients to translate requirements into robust solutions while mentoring junior engineers and improving engineering practices.

Build and scale Phantom’s data infrastructure for analytics, experimentation, and decision-making. Responsibilities include designing high-volume data architecture, leading data quality and observability initiatives, developing scalable data models, building A/B testing infrastructure, partnering cross-functionally, and mentoring teams. The role requires expertise in Snowflake, dbt, SQL, Python, data architecture, data modeling, and experimentation systems, with blockchain experience preferred.

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.

Own production Python data pipelines, ETL/ELT workflows, and the ontology powering an AI platform. Integrate warehouses and business applications with graph databases and vector stores, while managing deployment tooling, cloud infrastructure, monitoring, and security. The role begins as hands-on individual contribution and develops toward technical leadership through mentoring and architecture ownership.

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

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, 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.

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.

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.