Senior Data Engineer

TrueNorth
Manchester, UK
1 day ago
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Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Working hours
Regular working hours

Tech stack

Application Programming Interfaces (APIs) Airflow Amazon Web Services Amazon S3 Business Analytics Applications Data Analysis Microsoft Azure Big Data BigQuery Databases Data as a Services Data Architecture
+25 more
Information Engineering Extract Transform Load (ETL) Data Security Data Systems Data Warehousing Relational Databases JSON Python (Programming Language) Performance Tuning Role-Based Access Control Power BI Azure Data Lake Tableau (Software) Workflow Management Systems Data Processing Data Storage Technologies Cloud Platform System Azure Data Factory Sql Optimization Snowflake Data Lakes Semi-structured Data AWS Glue Data Pipelines Amazon Redshift

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., * 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

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

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Good distractions

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