Top Data Engineer Jobs in Manchester

The Data Engineer will build robust data pipelines, manage data ingestion processes, optimize performance, and collaborate with teams to ensure data reliability in a financial services context.

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.

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.

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.

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.

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.

Build and maintain large-scale batch and streaming data pipelines and analytics infrastructure for Roku's CTV advertising auction platform. Deliver high-quality datasets for advertiser and publisher analytics, support experimentation and auction simulation, implement data quality and observability, and partner with product, analytics, and data science teams to model data for marketplace optimization and reporting.

Entry-level data engineer to follow a structured 12-month development programme. Build internal reporting, reusable engineering frameworks, documentation, and tooling while learning SQL, Python, Databricks and cloud data services. Support senior engineers, join code reviews, shadow customer workshops, and progressively contribute to client projects.

Build and maintain BigQuery data models and Python data pipelines using Dataform and Airflow. Implement data quality checks, observability, and CI/CD; containerize workloads with Docker; support Data Science productionization and contribute to Looker reporting.

Design, build and maintain cloud-native ETL/ELT pipelines and data lake solutions using dbt, Snowflake/Databricks and AWS services. Optimize SQL and data models for performance and cost, create dashboards, follow CI/CD and software engineering best practices, and collaborate with architects, engineers and clients to deliver production-ready enterprise data solutions.

Support design, development and maintenance of data product pipelines for regulatory security monitoring and compliance. Assist with data ingestion, telemetry, automation of control monitoring, and collaborate with engineers and security specialists while learning modern engineering practices.

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.

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.

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 operate a new AI-focused data platform: design ingestion, transformation, semantic/ontology layers, storage, and DAG-based orchestration. Implement pipelines, select databases and orchestration tools, ensure performance, observability, and data quality, and collaborate with product and AI teams while remaining hands-on in coding and production operations.

Build and maintain cloud data pipelines and models, improve data quality, governance and observability, support self-service analytics, partner with Finance and Tech to deliver scalable, secure data solutions and contribute to platform architecture and engineering standards.

The role involves developing data pipelines, managing metadata and data quality in Azure, ensuring compliance with data governance standards, and collaborating with data teams.

Develop and maintain data pipelines, optimize solutions using AWS services, and ensure data quality and security while collaborating with cross-functional teams.

As a Data Engineer, you will develop and manage data pipelines, ensuring reliable data ingestion and delivery while optimizing for simplicity and performance.

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.

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 maintain Microsoft Fabric-based data pipelines, lakehouses, and warehouse solutions. Support migration of legacy systems, optimise ETL/ELT processes (PySpark, SQL, Python), enforce data quality, governance and security, support semantic models/Power BI reporting, troubleshoot platform issues, and contribute to data architecture standards and documentation.

The Data Engineer will design and deploy production data pipelines, connect structured and unstructured data on AWS and collaborate with various teams to solve client problems.

Build production AI-powered tools, data pipelines, and internal full-stack applications that automate incident investigation, surface anomalies, and enable AI agents to interact safely with platform systems. Work across Python, TypeScript, Go and SQL, deploy on Docker/Kubernetes/AWS, and collaborate with TechOps, Connectivity, and Data teams to own, monitor, and improve shipped systems.

Design, build, and maintain scalable data pipelines and Airflow orchestration; deliver Databricks-based data engineering solutions; support data science workflows; collaborate via GitHub, code reviews, and shared engineering standards; ensure reliable, observable, and maintainable pipelines.