Lead Engineer - Backend (Data Platform)

• ClickHouse — self-hosted and ClickHouse Cloud for high-volume analytics and SaaS reporting

• ClickHouse — self-hosted and ClickHouse Cloud for high-volume analytics and SaaS reporting
• BigQuery — large-scale analytics and data processing
• Bigtable — low-latency NoSQL serving
• Redis — caching, queues, and distributed coordination
• Kafka — high-volume real-time event streaming
• GCP — GCS, Pub/Sub, Dataflow, IAM, Secret Manager, GKE and VMs
• Java — Spring Boot and JVM-based backend services
• Apache Beam / Dataflow — distributed event and batch processing
• Go — adjacent SQL and reporting services

• Warehouse-native experiment computation in customer Snowflake/BigQuery environments
• Cross-platform visitor profiles and identity
• First-party cohorts and saved audiences
• Unified metrics across internal and customer warehouses
• Converging Wingify and AB Tasty analytics onto a common data foundation

• Own the technical architecture and design of significant backend/data-platform workstreams.

• Own the technical architecture and design of significant backend/data-platform workstreams.
• Design distributed systems for high-volume event ingestion, analytical querying, data processing, and low-latency serving .
• Make architectural decisions around data modelling, storage engines, partitioning, query patterns, caching, streaming, reliability, and cost .
• Evaluate trade-offs across ClickHouse, BigQuery, Bigtable, Redis, Kafka, and other technologies based on workload characteristics.
• Drive designs that can evolve as the Wingify and AB Tasty platforms converge.

• Remain hands-on with Java and production backend code .

• Remain hands-on with Java and production backend code .
• Build and evolve backend services, query engines, APIs, data pipelines, and distributed processing systems.
• Write and optimise complex analytical SQL and troubleshoot performance bottlenecks.
• Solve difficult production problems involving slow queries, data correctness, pipeline failures, latency, throughput, and infrastructure .
• Work with Kafka, Pub/Sub, Dataflow, GCS, ClickHouse, BigQuery, Bigtable, and Redis at production scale.
• Own critical systems through design, implementation, deployment, observability, and operation .

• 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.