Top Data Engineer Jobs in Edinburgh

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 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.

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.

The Software Engineer will develop data pipelines, manage cloud infrastructure, and collaborate with AI scientists to enhance data acquisition for model training.

Design, build, and maintain scalable batch and streaming data pipelines. Write and review code (Python, SQL, Spark), implement data quality checks, monitor pipelines, and collaborate with analysts, data scientists, and product managers to deliver reliable, documented data solutions.

Operate and architect large-scale distributed data infrastructure across Kafka, Hadoop HDFS, Ceph, SQL Server, bare-metal systems, and hybrid cloud environments. Responsibilities include capacity planning, reliability engineering, automation, observability, incident response, backup and recovery, platform tooling, and architecture decisions. The role requires expertise with petabyte-scale systems or billions of daily events, mentoring engineers, and shaping infrastructure standards across engineering organizations.

Serve as the technical authority for enterprise customers in data security, owning the technical sales process. Deliver technical presentations, demos, and whiteboard sessions, advise on AI/ML-driven data protection, and drive outcomes through value-based selling with partners and GTM teams.

As a Data Engineer, you will design, build, and maintain data pipelines and infrastructure to support data-driven decision-making in a collaborative environment.

The Data Engineer will design and support data warehouses, create analytics dashboards, perform data quality analysis, and identify process improvements. Responsibilities include collaborating with stakeholders to develop insights and ensure data governance.

The Data Engineer will build and manage scalable data pipelines and databases, collaborate on architecture, and drive data systems towards real-time capabilities in a fast-paced, innovative environment.

Leads the design and scaling of Databricks-based data, analytics, and AI platforms supporting global customer operations. Builds enterprise data pipelines, machine learning, NLP, generative AI, conversational AI, dashboards, and operational intelligence solutions. Establishes governance, data quality, CI/CD, reliability, and AI lifecycle standards while partnering with product, operations, engineering, workforce management, and AI teams. Provides technical leadership, coaching, and mentorship to engineers and data scientists.

Support ledger migration and data lake cleanup by extracting, transforming, reconciling and validating large datasets. Develop and validate SQL queries, batch jobs and processing logic, perform regression testing, document mappings and validation rules, and collaborate with engineering, product and business teams to ensure production readiness and post-migration validation.

Design, build, and maintain scalable batch and streaming data pipelines, internal tools, and APIs to support analytics, product features, and ML. Collaborate with product and engineering to improve data infrastructure, ensure reliability, testing, and observability in a cloud-native environment.

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.

Build large-scale data pipelines and infrastructure for frontier AI model training. Develop data-processing models such as classifiers, quality filters, and labeling systems; design deduplication, quality scoring, labeling, and augmentation strategies; and create reliable tooling for researchers to explore and train on massive datasets. The role also evaluates how data quality and composition affect model outcomes, with web crawler experience as a bonus.

Migrate SQL Server data products and analytical workloads to a modern cloud platform. Redesign legacy SQL, ETL processes, and business logic; build scalable pipelines and analytical models; validate data quality; collaborate with stakeholders; and improve platform reliability, performance, cost efficiency, and AI-assisted data engineering automation.

Design, build and maintain scalable, automated cloud data solutions and pipelines. Provide technical leadership to a small engineering squad, own complex backlog tasks, develop data frameworks and standards, and ensure code quality via reviews and pair programming using Databricks, Python, SQL and Spark.

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

Design, deploy, and maintain AWS and Databricks data infrastructure, data lakes, and scalable ETL pipelines. Build custom ingestion services and connectors using Airbyte, Go, and Python; optimize performance, reliability, monitoring, and data quality. Implement cloud data governance, security, access controls, encryption, and compliance practices while collaborating with platform engineers, analysts, and stakeholders.

Build, optimise and stabilise AWS-based data pipelines (S3, Glue, Redshift, Lake Formation). Resolve technical debt, support the Architect on target-state design, collaborate with client engineering teams to transfer knowledge, and deliver production-ready platforms in regulated, high-volume gaming environments.

Build and operate Python-based data ingestion, ETL, detection, and forensic artifact extraction pipelines for a mobile EDR platform. Productionize threat detection prototypes, optimize large-scale data storage and queries, deploy services on AWS, and establish monitoring, validation, fault tolerance, and data quality controls. Collaborate closely with security researchers to develop reliable tooling and scalable systems for identifying mobile spyware and other threats.

Design, build, and scale cloud-native data warehouses and robust ETL/ELT pipelines (primarily BigQuery). Optimize performance, implement partitioning and cost controls, ensure data governance, observability, and regulatory reporting support, and collaborate with analytics and engineering stakeholders.

Own and operate Databricks production data pipelines for a predictive CRM platform: monitoring, incident response, cost and performance optimization, migrating legacy ETL, onboarding customers, enforcing Delta Lake architecture and multi-tenant isolation, and applying CI/CD, security, and observability best practices to ensure reliable, scalable data delivery.