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# Data Engineering Manager

- **Company:** PEI Group
- **Location:** London, United Kingdom
- **Experience:** Experienced
- **Salary:** £71,770
- **Contract:** Permanent contract
- **Skills:** Unity 3d, Application Programming Interfaces (APIs), Agile Methodology, Data Analysis, Microsoft Azure, Code Review, Information Engineering, Data Governance, Data Structures, Data Systems, Software Debugging, Apache Hive, Microsoft Dynamics, Role-Based Access Control, Azure Data Lake, Software Engineering, Azure Data Factory, System Availability, Delivery Pipeline, Data Lakes, Pyspark, Git Flow, Data Lineage, Data Management, Data Delivery, Key Vault, Databricks
- **Published:** November 8, 2026
- **Apply:** https://www.adzuna.co.uk/jobs/details/5957973346

## About the Role

* 8+ years of hands-on experience in data engineering or software development, with significant exposure to data platforms and pipelines.
 * A minimum of 3 years of people management experience; has built and operated teams of highly skilled engineers and analysts.
 * Experience with hiring, mentoring, upskilling, and performance management.
 * Proven track record of successfully managing and leading multi-disciplinary engineering teams.
 * Solid understanding of the software and data delivery lifecycle.

Technical Skills

 * Strong experience with Databricks (PySpark/Spark SQL, Delta Lake, Unity Catalog, medallion architecture, Databricks Workflows).
 * Working knowledge of Microsoft Dynamics 365 data structures, Dataverse, and common integration patterns (Synapse Link for Dataverse, Dataverse APIs).
 * Solid understanding of the Azure data ecosystem (Azure Data Factory, ADLS Gen2, Azure DevOps, Key Vault).
 * Strong grasp of testing methodologies, CI/CD pipelines, and data quality automation.
 * Experience embedding data governance practices - cataloguing, lineage, RBAC, and regulatory compliance (e.g. GDPR)., * Exceptional delivery management skills, including planning, executing, and delivering complex data projects across multiple workstreams.
 * Ability to operate in an Agile environment with a start-up mentality and comfort in an evolving, unstructured environment.
 * Must be a compelling and clear communicator, able to represent the team to internal and external audiences with differing levels of technical fluency.
 * Driving a high-performance, collaborative, and inclusive team culture in a fast-paced environment.

## Description

As a Data Engineering Manager, you will provide technical and delivery leadership to a multi-disciplinary team of data engineers, QA engineers, data analysts, and Dynamics 365 developers. You will drive coding standards, delivery excellence, and continuous improvement across the data platform. This is a leadership-focused role with an 80% managerial and 20% hands-on split.

Roles and Responsibilities

 * Collaborate with Product, Project, and Business stakeholders to plan, prioritise, and deliver data initiatives on time and to a high standard, with adherence to standard processes.
 * Manage the delivery pipeline across multiple workstreams, balancing capacity, dependencies, and competing priorities while maintaining a high bar for quality.
 * Own and enforce coding standards, pull request review policies, testing practices, and branching strategies across the team.
 * Drive data quality, governance, and platform reliability - including data cataloguing, lineage tracking, PII handling, and data quality SLA ownership.
 * Optimise data solutions for maintainability, quality, performance, security, and scalability.
 * Provide technical leadership and direction across Databricks and Dynamics 365 workstreams, setting clear expectations and objectives for individuals and the wider team.
 * Hire, mentor, and performance-manage engineers and analysts at all career levels, fostering growth, accountability, and a culture of continuous learning.
 * Collaborate cross-functionally with data science, product, finance, and business operations teams to deliver data-driven solutions.
 * Contribute hands-on through architecture reviews, code reviews, prototyping, and debugging complex production issues - leading by example and staying close to the technology without being on the critical path.
 * Drive a continuous improvement process - introduce tooling, automation, and process changes grounded in real data analysis and team feedback.
 * Research and evaluate new technologies and trends in data engineering, recommending pragmatic improvements and innovations.
 * Prioritise and manage ad-hoc requests in parallel with ongoing projects.