Top Data Engineer Jobs in Manchester

Build and maintain near-real-time and batch regulatory reporting systems. Migrate legacy SQL Server solutions to GCP BigQuery, design cloud-native data models and ETL/ELT pipelines, implement validations, monitoring, CI/CD and IaC, apply AI-assisted tooling, perform QA and documentation, and collaborate with senior engineers to ensure accurate, traceable regulator-ready outputs.

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.

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.

Build and maintain real-time and batch data pipelines, consolidate data into data lakes/warehouses, implement ETL/ELT and data modeling best practices, develop transformations and notebooks, create interactive reports, and collaborate with engineers on data quality, testing, validation, and documentation.

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.

Design and deploy production data pipelines from ingestion through consumption using AWS cloud technologies. Build ETL processes, data warehouses, and data lakes; move and integrate structured and unstructured data from multiple sources. Write scripts, extract data through APIs, and develop SQL queries using Java, Python, Scala, Spark, and AWS-native services. Collaborate with clients, stakeholders, product teams, designers, and engineers in agile delivery environments.

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 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, 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 and enhance real-time data platform infrastructure and services. Develop low-latency, highly available streaming and storage solutions using Golang/Python, Kafka, Flink and GCP. Support release automation, monitoring, developer tooling, testing, and QA for high-volume transactional systems.

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 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.

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

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.

Leads architecture and development of large-scale batch and real-time data platforms and pipelines. Ensures data quality, governance, reliability, observability, scalability, and cost efficiency while building self-service capabilities. Supports analytics, machine learning, and generative AI teams with trusted data infrastructure. Establishes engineering standards, mentors engineers, and drives platform health across multiple teams. The role requires expertise in data integration, distributed processing, data modeling, cloud platforms, CI/CD, and infrastructure automation.

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 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.

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

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.