Data Engineer (Mid‑Level), Global

Overview

As a Mid-Level Data Engineer in London, you will build, operate, and scale our enterprise data platform to support analytics, reporting, and AI-enabled use cases. You’ll work within the Data Engineering & Business Intelligence team, collaborating with analysts and stakeholders while taking ownership of data pipelines. The role emphasizes independence, reliability, and fast execution in a dynamic environment. You will contribute to a scalable, governed data platform on Azure, enabling data-driven decisions. This is a hands-on role with a clear path to impact across analytics and AI initiatives.

Pay / Benefits

• above market total compensation package
• comprehensive health and welfare
• retirement benefits
• paid leave
• training and development opportunities
• recognition and global collaboration

• Design, build, and maintain scalable data pipelines using Python and PySpark on Azure
• Develop and operate batch and incremental ETL pipelines with Azure Data Factory and store data in Azure Data Lake Storage Gen2
• Implement SQL- and Spark-based transformations to create curated datasets for reporting and analytics
• Own assigned pipelines and datasets, including monitoring, troubleshooting, and performance tuning in production
• Work with Azure Synapse to support analytical workloads and data consumption patterns
• Collaborate with analysts and stakeholders to translate data requirements into practical solutions
• Prepare data for advanced analytics and AI use cases, ensuring quality, consistency, and documentation
• Apply data governance, security, and engineering standards for maintainable and scalable solutions
• Participate in code reviews and platform improvement initiatives
• Identify data quality issues and pipeline risks, communicating them in a fast-paced environment
• Develop and maintain PySpark notebooks and jobs for ingestion, transformation, and curation
• Create and modify Azure Data Factory pipelines for batch/incremental ingestion
• Implement Spark transformations that write to Azure Data Lake Gen2 with established structures
• Create SQL views and tables in Azure Synapse to support analytics
• Respond to pipeline failures, data validation issues, and operational alerts
• Perform basic Spark performance tuning within architectural patterns
• Validate data outputs with business partners and address defects
• Commit code with Git, follow branching standards, and participate in PR reviews
• Update pipeline and runbook documentation and manage backlog items in sprints

• Bachelor’s degree in Engineering, Computer Science, Data Analytics, or related field
• 3–5 years of data engineering or analytics engineering experience
• Proficiency in Python for data pipelines and PySpark
• Proficiency in SQL for data querying and transformation
• Strong understanding of ETL/ELT, data transformations, and data integration
• Experience analyzing enterprise data sources to identify relationships and business rules
• Experience building solutions on Microsoft Azure with Azure Data Factory, Azure Synapse, and Azure Data Lake Storage Gen2
• Experience with source control and CI/CD (GitHub or Azure DevOps)
• Knowledge of data modeling (fact and dimension tables)
• Strong communication and collaboration skills in a fast-paced environment
• Experience working in Agile environments
• Experience with Jira or similar project tracking tools
• Travel up to 10% (may increase over time)

• strong communication
• collaboration across teams
• self-starter mindset
• Python
• PySpark
• SQL