Data Engineering Manager

WSS Associates is seeking a Data Engineering Manager who is comfortable engineering data platforms and developing the engineers who build on them. This hybrid role involves hands-on contributions to data engineering deliverables while leading and mentoring a team of 5–8 data engineers and analysts.

Responsibilities:

• Lead the design, build, and continuous improvement of scalable data pipelines, Lakehouse architectures, and data products on Databricks and Azure
• Ensure engineering and architectural standards for performance, reliability, scalability, and cost optimisation are consistently delivered
• Remain hands-on by contributing to high-impact data engineering projects, code reviews, architecture reviews, and complex troubleshooting
• Champion data quality, observability, security, and governance best practices across the data platform and engineering lifecycle
• Manage a team of 5–8 data engineers, including hiring, onboarding, performance management, compensation recommendations, and retention
• Provide coaching, mentoring, and career development planning for team members
• Foster a high-performing, inclusive culture focused on accountability, collaboration, technical excellence, and continuous learning
• Own planning, prioritisation, and delivery of the data engineering roadmap
• Establish and refine Agile delivery practices, including sprint planning, estimation, retrospectives, and work intake processes
• Identify and manage risks, dependencies, and blockers across projects
• Own operational excellence, including SLAs, production support, incident management, and post-incident reviews
• Build trusted relationships with business and technology stakeholders
• Translate business objectives into prioritised data engineering initiatives
• Collaborate closely with Architecture, Data Engineering, AI, Governance, and Product teams to deliver end-to-end data solutions
• Communicate technical concepts, trade-offs, roadmaps, and progress effectively to both technical and executive audiences

Requirements:

• Significant hands-on Data Engineering experience with Databricks and Azure
• Strong experience with Spark, Lakeflow, Delta Live Tables (DLT), Delta Lake, Lakebase/Postgres, and Unity Catalog
• Proven experience managing and developing Data Engineering teams
• Track record of delivering production-grade data platforms and pipelines at scale
• Strong understanding of reliability, security, governance, and cost management
• Ability to lead complex technical initiatives through influence rather than authority
• Excellent communication, stakeholder management, and prioritisation skills
• Experience with modern data architecture patterns including Lakehouse, Medallion Architecture, Data Mesh, DataOps, and metadata/configuration-driven frameworks
• Experience leading technology transformation initiatives and platform evolution
• Familiarity with CI/CD and Infrastructure as Code for data platforms
• Experience with Azure DevOps, Databricks Asset Bundles (DABS), GitHub Actions, or equivalent tooling
• Experience working across global teams and multiple geographies/time zones
• Experience leading cross-functional initiatives in large-scale data organisations