Top Data Engineer Jobs in Edinburgh

Build and govern canonical, semantic, and analytical data products for Private Banking and Wealth Management. Responsibilities include designing cross-domain data models, developing scalable data pipelines, implementing data quality checks and SLAs, documenting reusable data assets, and collaborating with architects, engineers, BI teams, data scientists, and business stakeholders on AWS cloud data platforms.

Designs and manages research data pipelines and scalable infrastructure across clinical studies, sensor platforms, and HPC environments. Owns the data lifecycle, develops ETL/ELT and data-quality tools, maintains storage and security systems, and establishes data architecture standards. Collaborates with AI engineers and researchers to support machine learning, signal processing, and clinical validation applications. The role requires strong Python, SQL, database, automation, and CI/CD expertise, with medical device or algorithm experience preferred.

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.

Design and maintain a trusted, secure, governed database of livestock data from low- and middle-income countries. Clean, harmonize, document, and structure complex datasets to support evidence-based decision-making and AI-ready insights. The role leads data quality and governance practices across complex, regulated data environments and requires strong organisation, attention to detail, and a service-oriented approach.

Build and maintain scalable data-focused REST APIs, pipelines, and fraud recommendation applications. Develop batch and streaming ingestion using Python, SQL, PostgreSQL, Redis, and optionally PySpark. Deploy services on Kubernetes with KONG and ArgoCD, ensure reliability and data quality, monitor applications, troubleshoot issues, and collaborate with engineers, analysts, data scientists, and product managers in an agile environment.

Build, maintain, and release reliable cloud software for the Customer Data Platform. Lead complex feature delivery, make architectural decisions, improve code quality, testing, observability, security, and operational reliability. Collaborate with Product, Design, and adjacent engineering teams, participate in on-call support, contribute to planning and OKRs, and mentor engineers through pairing, reviews, and technical guidance.

Design, build, deploy, and optimize scalable AI and machine learning systems using large datasets. Develop predictive models, recommendation systems, personalization engines, forecasting solutions, generative AI applications, and production ML pipelines. Collaborate with data, backend, and product teams to integrate models into applications, while monitoring performance, data quality, drift, reliability, and cost. Establish best practices for evaluation, experimentation, reproducibility, and deployment, and research emerging AI technologies.

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.

Build end-to-end data applications, APIs, internal tools, and user interfaces using AI-assisted development. Work across Python backends, React frontends, data models, Databricks, dbt, pipelines, cloud infrastructure, testing, security, and performance. Review AI-generated code, establish guardrails, improve engineering workflows, and help shape application architecture and technical direction.

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.

Lead mechanical engineering design and technical delivery for mission-critical data centre projects. Develop cooling, ventilation, automation, controls, and ancillary systems while coordinating with electrical and multidisciplinary teams. Produce and review detailed designs, supervise engineers, allocate resources, manage deadlines, support client relationships, and apply building physics, CFD, sustainable design, CAD, BIM, and parametric tools.

Leads enterprise data engineering strategy and delivery across Databricks, dbt, Azure, Python, and SQL. Defines architecture and engineering standards while driving modernization, automation, security, resilience, governance, and compliance. Influences stakeholders, coaches engineering teams, simplifies technology landscapes, and aligns data, cloud, analytics, and AI investments with business outcomes in a regulated environment.

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.

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.

Leads electrical design for hyperscale and mission-critical data centers across Europe. Responsibilities include managing HV/MV/LV systems, substations, utilities, consultants, permitting, project delivery, construction support, commissioning, and integrated systems testing. The role provides client-side technical leadership, reviews designs and project documents, coordinates multidisciplinary teams, manages risks and milestones, and ensures compliance with standards and operational requirements.

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.

As a Senior Data Science Engineer, you'll lead data science projects, develop algorithms, analyze datasets, and mentor junior team members to enhance trading operations and strategies.