Data Engineer (Mid-Level)

Salary: £66,000 - 106,000 per year

Requirements

• We require a bachelors degree in Engineering, Computer Science, Data Analytics, or a related field, or equivalent experience.
• We require 3–5 years of experience in data engineering or analytics engineering.
• We require proficiency in Python for building and maintaining data pipelines, automation, and data processing workflows, including PySpark.
• We require proficiency in SQL for querying, transformation, and analytical data processing.
• We require a solid understanding of ETL/ELT pipelines, data transformation patterns, and data integration concepts.
• We require experience analyzing enterprise data sources to identify data relationships, transformations, and business rules.
• We require experience building solutions on Microsoft Azure, with exposure to Azure Data Factory, Azure Synapse, Azure Data Lake Storage Gen2, and related analytics services.
• We require experience working with source control and CI/CD workflows using tools such as GitHub or Azure DevOps.
• We require working knowledge of data modeling fundamentals, including fact and dimension tables.
• We require strong communication and interpersonal skills with the ability to collaborate across teams in a fast-paced environment.
• We require experience working in Agile development environments.
• We require experience using collaboration and project tracking tools such as Jira or similar tools.
• We require the ability to travel up to 10% as needed, with the possibility of increased travel as the business evolves.

Responsibilities

• We design, build, and maintain reliable, scalable data pipelines using Python and PySpark on the Microsoft Azure data platform.
• We develop and operate batch and incremental data pipelines using Azure Data Factory for orchestration and Azure Data Lake Storage Gen2 as the primary data store.
• We independently implement SQL- and Spark-based transformations to produce curated datasets that support enterprise reporting, analytics, and downstream consumption.
• We take ownership of assigned data pipelines and datasets, including monitoring, troubleshooting, and performance optimization in production environments.
• We work with Azure Synapse, dedicated or serverless where applicable, to support analytical workloads and data consumption patterns.
• We collaborate with business analysts and cross-functional stakeholders to translate data requirements into practical, working data solutions.
• We prepare and structure data to support advanced analytics and AI-enabled use cases by ensuring data quality, consistency, and documentation.
• We apply established data governance, security, and engineering standards to ensure compliant, maintainable, and scalable solutions.
• We participate in code reviews, technical discussions, and platform improvement initiatives as active contributors.
• We proactively identify data quality issues, pipeline risks, and improvement opportunities, and communicate them clearly in a fast-paced environment.
• We develop and maintain PySpark notebooks and jobs to ingest, transform, and curate data within the enterprise data platform.
• We build and modify Azure Data Factory pipelines for batch and incremental data ingestion.
• We implement Spark-based transformations that write curated datasets to Azure Data Lake Storage Gen2 using established folder structures and naming conventions.
• We create and maintain SQL views and tables in Azure Synapse to support analytics and reporting use cases.
• We respond to pipeline failures, data validation issues, and operational alerts.
• We perform basic performance tuning of Spark jobs within established architectural patterns and standards.
• We validate data outputs with business partners and address data defects or discrepancies.
• We commit code using Git, follow branching standards, and participate in pull request reviews.
• We update documentation for pipelines, datasets, and operational runbooks as changes are made.
• We execute assigned backlog items within sprint timelines and raise risks or blockers early.
• We perform additional duties as assigned by management.

Technologies

• AI
• Azure
• Business Intelligence
• CI/CD
• Cloud
• DevOps
• ETL
• Git
• GitHub
• Support
• JIRA
• Python
• PySpark
• SQL
• Security
• Serverless
• Spark
• Hardware
• Machine Learning

More

We are Vantage Data Centers, a global provider powering, cooling, protecting, and connecting the technology of hyperscalers, cloud providers, and large enterprises across North America, EMEA, and Asia Pacific. This role is based in our London office with a flexible hybrid schedule of 3 days on site and 2 days from home. You will join our Data Engineering & Business Intelligence team and help build, operate, and scale our enterprise data platform in support of analytics, reporting, and AI-enabled use cases. Our Technology & Systems department drives technological innovation across IT, software development, OT/automation systems, and business process improvement, and we work closely with Sales, Construction, Operations, and Corporate Functions. We value collaboration, speed to value, financial and execution discipline, and a culture of no ego and no arrogance. We offer an above-market total compensation package, comprehensive health and welfare, retirement, and paid leave benefits, along with recognition, training, development, and opportunities to contribute to our company and community.

last updated 36 week of 2026

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