Top Data Engineer Jobs in Manchester

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.

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.

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.

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.

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.

Design, build, and own scalable data pipelines from ingestion through analytics and incentive payouts. Lead data platform architecture, including Snowflake, dbt, Iceberg, and S3 workflows. Establish data quality, lineage, observability, performance, cost, and SLA standards. Partner with engineering and analysts to support telematics accuracy and driver incentive reporting. Develop maintainable data models and transformation layers while using AI coding tools to improve development speed and quality.

Build and evolve CluePoints’ enterprise data platform using Microsoft Fabric and Azure. Responsibilities include migrating Azure SQL and ADF workloads, integrating SaaS and web data, developing scalable ingestion and transformation pipelines, maintaining Kimball dimensional models, and establishing CI/CD, data quality, lineage, security, and monitoring. The role partners with analytics leadership and supports trusted Power BI reporting across multiple business functions.

Own and develop the company’s Power BI reporting environment, including semantic models, dashboards, advanced DAX, Power Query transformations, governance, deployment, and performance optimization. Collaborate with Data Engineering and business stakeholders to define KPIs, validate data, improve reporting quality, and deliver scalable BI solutions. Support data architecture, data quality, ad hoc analysis, and continuous improvement across enterprise reporting assets.

Lead the architecture and scaling of Overstory’s data platform for AI-powered vegetation analysis. Design and operate large-scale geospatial and temporal data pipelines, orchestration systems, event-driven workflows, service-oriented architecture, data quality frameworks, and observability practices. Drive architectural standards, system design, scalability, reliability, and traceability while mentoring engineers and collaborating with data, ML, and product teams.

Designs and delivers scalable data pipelines and Lakehouse architectures using Databricks, Spark, PySpark, and Delta Lake. The role covers ETL/ELT, data governance, security, quality, performance optimization, cloud integration, and CI/CD. It involves technical leadership across customer programmes, architectural decision-making, stakeholder collaboration, mentoring, and supporting reliable production data solutions.

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.

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

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.

Build, automate, secure, and operate AWS cloud-based data platforms in a regulated UK government environment. Responsibilities include Terraform infrastructure-as-code, Python and Spark data workloads, Airflow orchestration, Jupyter support, GitLab CI/CD, Docker containerisation, monitoring, security tooling, platform reliability, documentation, and collaboration with engineering and integration teams.

Lead the technical direction and architecture for Arbor’s data platform, spanning transactional, analytical, and AI systems. Define target architectures, guide technical leads, support product delivery, and enable AI-first engineering through LLM orchestration, RAG, and real-time inference infrastructure. The role includes mentoring, architectural reviews, data governance, security, compliance, observability, DataOps, cloud platform cost optimization, and cross-functional collaboration with product and commercial leaders.

Own the end-to-end design, reliability, resilience, and recovery of production data ingestion systems and pipelines. Build batch and streaming ETL/ELT workflows, establish data quality checks and SLAs, improve monitoring and alerting, resolve platform issues, reduce technical debt, and optimize performance and cost. Collaborate with stakeholders, AI teams, and international expansion initiatives while supporting time-series, textual, and event-stream data workloads.

Own the design, delivery, and operation of complex data pipelines, datasets, and platform components. Translate business needs into scalable data models and reliable data products, improve data quality and observability, resolve production issues, and establish reusable engineering tools and standards. Collaborate with product and business stakeholders, contribute to architecture decisions, mentor engineers, and participate in on-call support.

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.