Engineering Manager - Data

We are looking for an Engineering Manager to strengthen the execution and people leadership of our Data Engineering organisation. You will lead a team responsible for building reliable, scalable and cost-efficient data platforms that support analytics, regulatory reporting, operational systems and AI/ML use cases. This role will translate the organisation's data strategy and platform roadmap into predictable execution while building a high-performing and engaged engineering team.

The candidate will have responsibilities across the following functions:

People and Team Leadership:

• Lead, coach and develop Data Engineers across multiple levels.
• Own performance management, career development, succession planning and retention.
• Drive hiring, onboarding and capability development.
• Build clear ownership, accountability and a strong engineering culture.
• Maintain team health through regular feedback, workload management and people pulse actions.

Delivery and Execution:

• Convert the platform roadmap into clear quarterly plans, milestones and measurable outcomes.
• Own delivery predictability, execution governance, dependency management and risk escalation.
• Coordinate execution across Product, Analytics, Finance, Risk, Security, Infrastructure and application engineering teams.
• Establish effective planning, design review, operational review and incident-management practices.
• Reduce unplanned work by addressing recurring incidents, operational gaps and manual dependencies.

Platform Reliability and Operational Excellence:

• Improve the reliability, availability, data quality and observability of critical data products and pipelines.
• Establish appropriate SLIs, SLOs, ownership and on-call practices for critical data services.
• Drive root-cause closure and ensure production learnings translate into engineering improvements.
• Strengthen security, governance, compliance and cost controls across the data platform.

Strategic Execution Priorities:

Partner with the VP of Data Engineering and Senior Staff Engineer to deliver three major priorities:

Data decentralisation and self-service:

• Enable domain teams to discover, onboard, publish and operate trusted data products.
• Establish clear ownership boundaries, data contracts, quality standards and reusable platform capabilities.
• Reduce dependency on the central Data Engineering team for routine data needs.

Platform cost transformation:

• Improve platform economics through workload optimisation and fit-for-purpose architecture.
• Support the transition from premium vendor-dependent solutions toward sustainable native and open technologies where appropriate.
• Establish cost visibility, accountability and unit economics for major workloads.

ML and AI platform enablement:

• Build the data foundations and engineering capabilities required for production AI/ML use cases.
• Partner with Data Science, Product and ML Engineering on data readiness, feature pipelines, governance and productionisation.
• Enable repeatable movement from experimentation to reliable production systems.

Requirements:

• 10+ years of software or data engineering experience, including 3+ years managing engineering teams.
• Strong experience building and operating large-scale data platforms or distributed systems.
• Hands-on understanding of data ingestion, batch and streaming processing, lakehouse or warehouse architectures, orchestration and data quality.
• Demonstrated experience managing platform roadmaps and complex cross-functional delivery.
• Strong people leadership across hiring, coaching, performance management and retention.
• Experience establishing operational excellence through observability, SLOs, incident management and root-cause prevention.
• Ability to balance delivery speed, platform reliability, technical debt and cost.
• Strong communication and stakeholder-management skills.
• Experience with AWS, Databricks, Spark, Kafka, Airflow and modern lakehouse technologies.
• Experience building self-service platforms or implementing data-product/domain-ownership models.
• Exposure to ML platforms, feature pipelines or production AI systems.
• Experience operating data systems in a regulated, financial-services or high-availability environment.
• Experience driving cloud or platform cost optimisation.