Senior Data Engineer

TrueNorth®; Manchester; On-site; GBP 60,000 - 90,000; Full time

Job summary

A fast-growing technology-driven organization in the United Kingdom seeks a Senior Data Engineer (Analytics) to build and scale a modern, cloud-based data platform. This role focuses on designing robust data pipelines and enabling self-service analytics. You will work across AWS and Azure, contributing to data lakes, warehouses, and BI tools. The ideal candidate has strong SQL skills, experience in cloud environments, and a solid understanding of data modelling principles. This organization values simplicity, maintainability, and scalability in its data architecture.

Qualifications

• Strong experience in a Data Engineering or Analytics Engineering role.
• Advanced SQL skills and solid understanding of data modelling principles.
• Experience building and maintaining data pipelines in cloud environments.

Responsibilities

• Design, build, and maintain scalable data pipelines using modern ELT frameworks.
• Develop and optimise analytics-ready datasets to support reporting.
• Monitor, troubleshoot, and optimise pipeline performance and query efficiency.

Skills

Data Engineering or Analytics Engineering

SQL

Problem-solving skills

Communication skills

Tools

AWS

Azure

Python

Azure Data Factory

Apache Airflow

Power BI

Job description

We are looking for a skilled Senior Data Engineer (Analytics) to play a key role in building and scaling a modern, cloud-based data platform for a fast-growing, technology-driven organisation.

This role sits at the intersection of data engineering and analytics, with a strong emphasis on designing robust data pipelines, developing high-quality datasets, and enabling self-service analytics across the business.

You will help shape a scalable data ecosystem that empowers teams to make data-driven decisions through accessible, reliable, and well-structured data. The environment prioritises simplicity, maintainability, and scalability, leveraging cloud-native tooling and configuration-driven approaches over complex custom builds.

Working across AWS and Azure, you will contribute to a modern data platform incorporating cloud data lakes, warehouses, and BI tools, supporting both internal analytics and customer-facing data solutions.

Key Responsibilities

• Design, build, and maintain scalable data pipelines using modern ELT frameworks (e.g. Azure Data Factory, Airflow or similar)
• Develop and optimise analytics-ready datasets to support reporting, operational insights, and downstream applications
• Work with a variety of data sources including APIs, relational databases, and semi-structured data stores
• Improve and maintain existing Python-based data workflows and orchestration processes
• Ensure data pipelines are robust, efficient, and support incremental processing
• Monitor, troubleshoot, and optimise pipeline performance and query efficiency
• Support and enable self-service analytics by delivering well-structured, trusted datasets
• Collaborate with engineering, product, and business teams to define and deliver data requirements
• Contribute to data architecture decisions, tooling selection, and platform improvements
• Implement and maintain data governance, security, and access controls (e.g. RBAC)
• Integrate with third-party systems and external data providers via APIs
• Support the delivery of embedded or customer-facing analytics solutions

Skills & Experience

Core Requirements

• Strong experience in a Data Engineering or Analytics Engineering role
• Advanced SQL skills and solid understanding of data modelling principles
• Experience building and maintaining data pipelines in cloud environments
• Strong problem-solving skills with the ability to translate business needs into data solutions
• Experience working with large-scale and/or complex datasets
• Familiarity with performance tuning and optimisation across data pipelines and queries
• Strong communication skills and ability to work cross-functionally

Technical Experience (examples)

• Cloud Platforms: AWS and/or Azure
• Data Storage & Processing: Data warehouses, data lakes, and databases (e.g. Redshift, Snowflake, BigQuery, S3, Azure Data Lake)
• ELT / Orchestration Tools: Azure Data Factory, Apache Airflow, AWS Glue, Matillion or similar
• Programming: Python (or similar for data processing and orchestration)
• BI & Visualisation: Power BI, Tableau, or similar tools
• Data Types: Structured and semi-structured data (e.g. JSON, document stores)
• Experience with data security and access control frameworks (e.g. RBAC, identity providers)
• Experience integrating external APIs and third-party data services
• Exposure to regulated or data-sensitive environments
• Experience evaluating and selecting data tools and vendors