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 enhance core data platform infrastructure and services supporting real-time streaming, cloud databases, distributed storage, system integration, release automation, and developer tooling. Develop scalable, low-latency solutions using Golang, Kafka, Flink, GCP, Spanner, and BigQuery. Support testing, monitoring, performance validation, quality assurance, and continuous platform improvement in a high-volume environment.

Build and maintain scalable cloud-based data pipelines and integrations using Snowflake, SQL, Python, dbt, and Windmill. Develop ELT processes, data models, API integrations, and automated workflows supporting regulatory reporting, business intelligence, and operational needs. Ensure data quality, testing, monitoring, documentation, and reliable deployments through GitHub and CI/CD. Collaborate with technical and business stakeholders while contributing to the evolution of the bank’s modern data platform.

Design and build scalable cloud-native data platforms, batch and streaming pipelines, data lakes, and integrated data services. Support analytics and AI/ML workloads while implementing DevSecOps, infrastructure as code, and reliability improvements. Collaborate with data scientists, engineers, architects, consultants, and clients to solve complex data challenges and translate requirements into technical solutions. The role supports Defence, Government, and National Security projects and requires high-level security clearance and on-site work three to four days weekly.

Designs, develops, and maintains scalable data structures, models, and automated pipelines for analytical and reporting needs. Integrates multiple source systems, transforms data into reusable datasets, monitors data quality, and implements validation and reconciliation processes. Collaborates with analysts and stakeholders to translate reporting requirements into datasets, maintains data documentation and lineage, and improves automation and efficiency. This is a UK-based remote role with occasional onsite attendance.

Develop and maintain GCP-based data lake and data warehouse infrastructure, including BigQuery, BigLake, Analytics Hub, and GCS. Build robust data-loading and transformation processes, establish development standards, optimize analytical processing, and document technical solutions. Collaborate with cross-functional teams to address complex data engineering challenges and support business-critical programs.

Develop and maintain customer-facing analytical reporting, data pipelines, and data models. Build Python applications, ensure data quality and consistency, collaborate with analytics engineers, and enable business teams to access actionable data. The role also involves cloud data warehouse optimization, infrastructure as code, software engineering best practices, and cross-functional product collaboration within an agile SaaS environment.

Design and develop scalable AI-powered solutions for complex enterprise data and analytics problems. Translate ambiguous business needs into algorithmic approaches, build AI architectures and model pipelines, integrate capabilities into enterprise platforms, evaluate emerging AI methodologies, and document solution designs. The role requires strong machine learning, statistical, mathematical, software development, systems architecture, and communication skills.

Design and build scalable AI-powered solutions for complex enterprise data and analytics problems. Translate ambiguous business needs into algorithmic approaches, develop model pipelines and data-processing architectures, integrate AI capabilities into enterprise platforms, evaluate emerging AI methodologies, and document solution designs. The role requires strong software development, machine learning, statistical, analytical, mathematical, and communication 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.

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.

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.

Design, build, and maintain scalable data pipelines and platform services to support analytics, ML, and product teams. Implement infrastructure-as-code (Terraform) and Kubernetes-based deployments, develop data services/APIs, and ensure data quality, observability, security, and compliance. Partner with cross-functional teams, document systems, and continuously improve performance and reliability.

Designs, builds, and maintains scalable data pipelines, datasets, and platform solutions. The role focuses on Microsoft Fabric, data warehousing, lakehouse architecture, data integration, dimensional modelling, CI/CD, testing, monitoring, and reliability. It includes ownership of complex technical solutions, architecture contributions, collaboration with business teams, and mentoring other engineers.

Leads the design, development, testing, integration, and optimization of scalable data-processing and persistence components. Establishes engineering and operational standards, ensures pipeline performance and compliance, troubleshoots systems, supports proposals, and collaborates with architects, operations, security teams, customers, and managers. Coaches and develops engineers while overseeing code quality, data models, testing, deployment readiness, and technical delivery.

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.

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.

Lead data engineering initiatives and manage a small engineering team while designing, building, and optimizing Databricks Lakehouse pipelines. Develop governed, scalable Azure data solutions using Python, PySpark, SQL, Delta Lake, and modern DevOps practices. Support SQL Server migration, data quality, testing, orchestration, monitoring, and enterprise analytics. Provide technical leadership, mentoring, stakeholder communication, planning, and delivery oversight across complex transformation projects.

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.

Leads a Data Products team designing, developing, and evolving foundational data products for energy-market modeling, analytics, and client-facing systems. Owns data product quality, reliability, governance, schemas, versioning, metadata, and data contracts. Provides technical leadership, collaborates with subject matter experts and platform engineers, establishes engineering best practices, and supports agile delivery and continuous improvement.