Head of Data Engineering
$90,000–$90,000 year
HybridManchester, England, United Kingdom
Job Summary
Lead and develop the Data Engineering team, building capability and resilience while strengthening engineering practices and technical standards. Own the core data platform, architecture, warehouse, and pipelines, ensuring solutions are scalable, reliable, and cost-effective. Establish strong engineering practices across source control, CI/CD, testing, code review, and monitoring. Translate the Data Strategy and business priorities into an effective engineering roadmap, collaborating with IT and Infrastructure on architecture and integrations. Identify opportunities to improve data performance and efficiency using emerging technologies. Manage delivery using agile ways of working and foster a culture of technical excellence.
Required Qualifications
- Demonstrable experience leading, developing and managing Data Engineering teams
- A strong track record of designing and delivering enterprise-scale data platforms, solutions and architectures
- A thorough understanding of modern ETL/ELT practices and experience designing scalable, reliable and maintainable data pipelines
- Advanced working knowledge of SQL and Python
- Experience working with modern cloud data platforms such as Microsoft Fabric, Snowflake, Databricks, Redshift or BigQuery
- Experience within the Microsoft data ecosystem
- Strong understanding of modern engineering practices including source control, CI/CD, automated testing, code review, monitoring and observability
- Strong experience of data modelling for reporting and analytics, using tools and approaches such as dbt or equivalent
- Strong understanding of data quality, governance, security and data management best practices
- Experience managing engineering delivery using agile ways of working
- Strong communication and stakeholder management skills, with the ability to explain technical decisions, risks and trade-offs to both technical and non-technical audiences
Desired Qualifications
- Experience integrating data with operational systems, including reverse ETL into platforms such as Salesforce
- Understanding of the Data Science / Machine Learning lifecycle and MLOps
- Experience using Infrastructure as Code approaches and tools such as Terraform or CloudFormation
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