Top Data Engineer Jobs in Manchester

Develop and maintain regulatory reporting systems using SQL Server and GCP BigQuery. Build ETL/ELT pipelines, migrate legacy solutions to cloud-native platforms, and implement data modelling, validation, monitoring, alerting, and automation. Ensure regulatory submissions are accurate, traceable, performant, and reliable. Participate in QA, documentation, code reviews, and technical collaboration while applying AI-assisted development tools to improve delivery quality and reduce manual effort.

Build and maintain secure, scalable data architectures and ETL/ELT workflows for a payments intelligence platform. Develop ingestion solutions using APIs, web scrapers, and SFTP; onboard client data; troubleshoot production issues; and contribute to Databricks-based data platform capabilities. Collaborate in an Agile squad, apply testing and CI/CD practices, create reusable Python libraries, and support continuous improvements across data engineering solutions.

Build and support core data-platform infrastructure for real-time streaming and high-scale distributed systems. Develop applications and services using Golang, Kafka, Flink, GCP, SQL, Spanner, and BigQuery; create automation and developer tooling; support Kafka integrations; improve platform reliability and performance; maintain clean code; and conduct functional, performance, and quality testing.

Work in multidisciplinary teams to design, build and maintain data pipelines, models and analytics products using SQL, Python and modern data tools. Contribute to cloud-based data solutions (AWS/Azure), apply software engineering and data governance practices, create visualisations, and collaborate with stakeholders in Agile delivery to produce maintainable, production-ready data services.

Designs, builds, and maintains scalable data pipelines, datasets, data models, warehouses, lakehouses, and integrations. Develops the Microsoft Fabric data platform and applies engineering practices including Git, CI/CD, testing, monitoring, documentation, and error handling. Collaborates with Reporting, Insights, Data Science, IT, and other technical teams to improve data quality, architecture, platform performance, and reliability.

Design and build production data pipelines, data warehouses, and data lakes on AWS. Develop ingestion, curation, and data movement solutions for structured and unstructured data using Java, Python, Scala, Spark, SQL, and AWS services. Extract data through APIs, write scripts and queries, collaborate with clients and cross-functional teams, and apply agile engineering practices to deliver scalable digital solutions.

Designs, builds, and maintains scalable AWS data platforms, ETL/ELT pipelines, data models, and processing workflows. Uses Python, SQL, infrastructure-as-code, CI/CD, monitoring, and data quality practices to deliver secure and resilient solutions. Collaborates with architects, security specialists, and clients; troubleshoots complex issues; establishes engineering standards; and mentors other engineers. The role also requires technical ownership and consideration of governance, access control, and regulated-environment requirements.

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.

Designs and develops scalable AI-powered solutions for enterprise data and analytics problems. Translates ambiguous business needs into algorithmic approaches, builds model pipelines and decision systems, integrates AI capabilities into enterprise platforms, evaluates emerging methodologies, and documents solution architectures. Requires strong software development, machine learning, statistical, mathematical, system design, communication, and large-dataset skills.

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.

Designs and maintains Microsoft Fabric data pipelines, lakehouses, warehouses, ETL/ELT processes, semantic models, and governance frameworks. Supports legacy data warehouse migration, platform optimisation, data quality, security, monitoring, troubleshooting, and self-service analytics. Collaborates with analysts, BI developers, stakeholders, and technical teams to deliver scalable, reliable enterprise data solutions and contribute to architecture standards and documentation.

Build and operate distributed ingestion and processing pipelines, ensure reliability and performance, define data contracts, add observability and testing, improve platform reliability and CI/CD, and participate in design/code reviews and incident retrospectives.

Design, build and maintain Azure-based data platform and pipelines (ETL, migrations, data models). Ensure data quality, integrity, monitoring and CI/CD. Support data warehouse, collaborate with analysts, and enable analytics and AI-driven insights across the business.

Develop and maintain real-time and batch ETL data pipelines using Python and SQL, optimise pipeline performance, build infrastructure-as-code and automation, support production systems, monitor application performance, document datasets and collaborate with engineering, product and operations teams. Participate in on-call rota after onboarding.

Support ML team by testing, validating and analysing behavioural product datasets, investigating data quality issues, writing SQL/Python checks and automations, documenting datasets, and supporting stakeholders and clients.

Lead the design and implementation of a scalable Snowflake-based data platform, establishing enterprise data governance, ingestion, transformation, and democratization standards. Optimize ELT pipelines, enable advanced analytics and AI, evolve infrastructure-as-code, and ensure compliance with privacy and security policies. Collaborate with cloud engineering, stakeholders, and Agile teams while guiding data strategy, technical design, and adoption of emerging technologies.

Develop and maintain enterprise Power BI and Tableau reporting solutions, data pipelines, models, dashboards, and analytics using Snowflake, SQL, dbt, and Alteryx. Analyze institutional and external data to support strategic planning, forecasting, recruitment targets, and executive decision-making. Ensure data quality, reporting governance, release standards, and stakeholder satisfaction. Provide technical guidance, training, and support across the University, contribute to emerging AI-enabled analytics, and potentially line-manage junior developers.

Build, maintain, and troubleshoot scalable AWS data pipelines using Python, PySpark, Spark, and Airflow. Perform root-cause analysis of pipeline and data-quality issues, apply dimensional modeling and slowly changing dimensions, and deliver governed data assets. Provision infrastructure with Terraform, containerize solutions with Docker, and manage GitLab CI/CD deployments. Work with AWS security, encryption, IAM, monitoring, and analytics services in a regulated government environment.

Design, build, maintain and evolve cloud data platform and ELT pipelines. Collaborate with stakeholders to provision, model and govern data, automate CI/CD, create data quality tests, and recommend new technologies.

Designs, builds, and maintains scalable data pipelines supporting data science and advanced analytics. Manages reliable Airflow orchestration, delivers data engineering solutions using Databricks, and contributes to GitHub-based collaborative development, code reviews, and engineering standards. Partners with data scientists, supports controlled feature-led delivery, investigates root causes, and develops practical solutions. The role also values adaptability, continuous learning, and exposure to MLOps concepts and tooling.

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 end-to-end Microsoft Fabric and Azure data-platform delivery, including pipelines, data models, CI/CD, security, governance, monitoring, optimization, Power BI integration, documentation, production handover, and stakeholder collaboration. Mentor engineers and help clients modernize legacy or proof-of-value platforms into secure, scalable production services.

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.