Lead Engineer - Backend (Data Platform)

Set technical direction for a platform area or major engineering workstream .

Break ambiguous product and platform requirements into clear technical designs and execution plans.

Review architecture and designs from other engineers and challenge assumptions constructively.

Establish engineering practices around reliability, scalability, observability, performance, and maintainability .

Mentor SSE-1/SSE-2 engineers and help them grow their system-design and production-engineering capabilities.

Partner closely with Product, Engineering, Data, and Infrastructure teams.

Influence architecture beyond your immediate projects without requiring formal people-management authority.

6+ years of hands-on backend/software engineering experience , with substantial experience building distributed or data-intensive systems.

Demonstrated ownership of architecture and system design for production systems at meaningful scale.

Strong hands-on Java experience, including modern Java and JVM backend systems.

Strong experience designing systems around analytical databases, data warehouses, and/or large-scale non-relational stores .

Deep understanding of data modelling, partitioning, indexing, clustering, query execution, caching, concurrency, and performance optimisation .

Strong SQL and analytical-querying experience.

Experience designing and operating streaming/event-driven architectures , ideally using Kafka or equivalent technologies.

Experience building batch and/or streaming data pipelines at scale.

Strong cloud engineering experience; GCP is preferred.

Experience operating production systems with clear SLOs, observability, failure handling, and performance requirements .

Strong debugging ability across applications, distributed systems, databases, pipelines, and infrastructure.

Experience mentoring senior engineers, reviewing technical designs, and influencing architecture .

Ability to balance long-term platform architecture with pragmatic delivery.

Deep production experience with ClickHouse , including schema design, MergeTree engines, partitioning, sorting keys, query optimisation, replication, and performance tuning.

BigQuery and Bigtable at significant production scale.

Apache Beam / Dataflow .

Snowflake or warehouse-native compute architectures.

Go , Vert.x, or other high-performance backend technologies.

Experience handling billions of events and/or TB–PB scale analytical datasets .

Experience with experimentation platforms, A/B testing, funnels, segmentation, identity resolution, cohorts, or behavioural analytics.

Kubernetes, Docker, CI/CD, Prometheus, Grafana, or GCP Cloud Monitoring.