Team Leadership: Lead, grow, and retain a team of 8–10 engineers. Own hiring, performance management, career development, and the health and delivery cadence of the team.
Technical Direction: Set technical direction for your domain — driving RFCs, design docs (HLD/LLD), and execution plans, and holding a high bar for engineering standards.
Data-Driven Decision Making: Ground team decisions in data. Define the right metrics, insist on measurement before and after changes, and use experimentation (A/B and quasi-experimental) to validate that changes actually move the numbers rather than shifting them around.
Applied Data Science Partnership: Work fluently with data scientists and ML systems — forecasting, optimisation, personalisation, and prediction — enough to challenge modelling choices, reason about trade-offs, and integrate models reliably into production systems.
System Architecture & Scale: Guide the design of highly concurrent, low-latency systems that process massive real-time throughput — event streams, microservices, and the data pipelines that feed them — while safeguarding data consistency and performance.
Operational Excellence: Own reliability and root-cause discipline for your domain. Champion staging/E2E validation, observability (tracing/metrics), and CI/CD so that issues are caught before customers feel them, and diagnosed with data when they aren’t.
Cross-Functional Collaboration: Partner closely with product, operations, data science, and peer engineering squads to align on strategy and deliver seamless, well-instrumented experiences.
Strategic Evolution: Balance short-term delivery against long-term architectural stability. Identify structural improvements to how the team measures, builds, and operates, and make the case for them with evidence.