Top Data Engineer Jobs in Edinburgh

Designs and maintains scalable ETL and ELT pipelines, data architectures, databases, and warehouses. Integrates data from APIs and third-party platforms while ensuring quality, security, governance, and reliability. Monitors workflows, improves processing and query performance, supports analytics and business intelligence initiatives, and documents technical processes. Collaborates with analysts, engineers, data scientists, and business stakeholders in a fully remote environment.

Provide technical leadership for ENSEK’s data ecosystem, owning data architecture, platform strategy, roadmap, and data mesh adoption. Establish standards for event-driven architecture, schema governance, data quality, observability, lineage, metadata, security, and cost management. Design governed data-sharing interfaces for customers and partners, solve complex engineering challenges, influence senior stakeholders, and mentor engineering teams.

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.

Lead the engineering, automation, security, resilience, and lifecycle management of enterprise data platforms across cloud and on-premises environments. Provide technical direction, improve platform reliability and performance, develop Infrastructure as Code and operational standards, resolve complex incidents, evaluate technologies, and mentor engineers. Work with customers and vendors to ensure effective platform adoption and participate in critical-service support arrangements.

The Senior Data Platform Engineer will manage Databricks infrastructure, develop cloud systems, collaborate with teams, and enhance machine learning workflows.

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 scalable ETL pipelines, data models, ingestion processes, and optimized data products using Azure Databricks and related Azure services. Develop raw, refined, and curated data layers, implement testing and secure ETL designs, monitor pipeline performance, resolve incidents, document solutions, and participate in Agile delivery and governance. Collaborate with architects, analysts, engineers, and stakeholders on modern data platform development.

Provides regional technical authority for BMS, EPMS, PLC, OT, and DCIM architectures across data centres. Defines engineering standards, governs complex designs, resolves high-impact technical risks, leads vendor and product strategies, and mentors engineering teams. Advises executives on technology roadmaps and ensures reliable, scalable mission-critical infrastructure.

Build AI-ready data infrastructure by designing event tracking plans, developing dbt models, and managing ingestion pipelines into Snowflake. Own data quality, metric definitions, documentation, semantic layers, and reporting consistency across Amplitude and Snowflake. Orchestrate workflows with Airflow and collaborate with product, engineering, and analytics teams to deliver reliable data products.

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.

Leads mechanical engineering delivery for complex data centre projects, designing resilient cooling, ventilation, automation, controls, and ancillary systems. Coordinates with electrical disciplines, manages engineers and resources, checks technical work, produces detailed designs, and oversees project timelines. The role requires client-facing leadership, technical supervision, design coordination, and knowledge of building physics, CFD, sustainability, and mission-critical engineering environments.

Build and operate OrderYOYO’s production data and AI platform, including Microsoft Fabric lakehouse solutions, scalable ingestion pipelines, semantic models, monitoring, data quality, and legacy-platform migrations. Develop with Python, PySpark, SQL, and T-SQL; manage performance, cost, and reliability; and collaborate with Finance, Product, executives, and engineering. Deliver applied AI and machine-learning capabilities such as RAG, agents, churn, recommendations, personalization, fraud, and risk models.

Design, build, and optimize Phantom’s streaming blockchain data platform, including scalable ETL pipelines, data models, schemas, quality frameworks, observability, and high-availability systems. The role delivers accurate, low-latency token, price, and spam data to engineering teams, collaborates cross-functionally, and contributes to team growth. Experience with backend systems, modern data engineering, and Web3 or on-chain data is expected.

Build interactive data visualizations, maps, dashboards, and 3D experiences that turn complex energy data into clear narratives. Work independently with marketing, policy, press, leadership, and data science teams using React, Next.js, Mapbox, WebGL, and Blender. Own projects end to end, select appropriate visualization techniques, communicate insights effectively, and rapidly adapt to varied project requirements.

Design and maintain reliable data solutions, lead a squad of engineers, develop scalable data pipelines, and ensure adherence to data standards.

Own the end-to-end delivery of applied machine learning products, from framing customer problems and building data pipelines to deploying, monitoring, and measuring model impact. Develop models for operational data using Python, SQL, dbt, Snowflake, and AWS; define evaluation and drift monitoring; conduct A/B tests or causal analyses; and partner with product and engineering teams to embed ML into customer workflows.

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.

Develop and optimize Supabase’s Data API based on PostgREST. Responsibilities include improving the PostgreSQL driver and performance, developing features, fixing bugs, resolving incidents and escalated support issues, improving observability, and debugging integrations across the Supabase platform. The role requires strong Haskell development, PostgreSQL and SQL expertise, Linux debugging, open-source contribution, and technical user support.

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.

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.