Top Data Engineer Jobs in Manchester

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.

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.

Lead the design, deployment, and operation of scalable data pipelines and cloud infrastructure for Kpler’s marine insurance initiative. Build containerized AWS services using Python, Docker, Kubernetes, Airflow, and Terraform; manage PostgreSQL and Kafka/Flink streaming systems; establish observability standards; provide technical direction; collaborate cross-functionally; and mentor engineers.

Own the company’s end-to-end data platform, including ingestion, S3 data lakes, ClickHouse, dbt transformations, orchestration, and BI/CRM data delivery. Improve reliability, historical accuracy, cost, and metric governance while defining and defending business KPIs. Partner with Customer Success, Sales, Product, and leadership to translate business questions into durable metrics and establish data standards. This is a senior individual contributor role focused on hands-on engineering, analytical rigor, and stakeholder communication.

Build and launch scalable data pipelines, products, APIs, and tools for proprietary market research. Develop big data processing systems, reusable software components, AI-ready data assets, cloud infrastructure, and governance controls. Partner with investment professionals, researchers, data scientists, and Compliance to deliver reliable, auditable, compliant solutions while optimizing system performance, cost, and reliability.

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.

Designs, develops, and supports secure cloud-based data integration solutions using Microsoft Fabric and Azure. Responsibilities include client discovery, requirements gathering, ETL and ELT development, REST API and SFTP integrations, data pipeline implementation, production monitoring, incident investigation, and reliability improvements. The role collaborates with architects, analysts, project managers, and delivery teams across multiple client engagements.

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.

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.

Leads electrical design for hyperscale and mission-critical data centers across Europe. Responsibilities include managing HV/MV/LV systems, substations, utilities, consultants, permitting, project delivery, construction support, commissioning, and integrated systems testing. The role provides client-side technical leadership, reviews designs and project documents, coordinates multidisciplinary teams, manages risks and milestones, and ensures compliance with standards and operational requirements.

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.

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.

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.

Lead large-scale technical initiatives and build scalable product functionality and infrastructure using TypeScript, React, and Node.js. Partner with product and design teams to develop privacy features for SaaS customers, make architectural tradeoffs, mentor engineers, support hiring, and establish engineering excellence across a growing organization.

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.

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.

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.

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.