Lead Engineer - Backend (Data Platform)

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.

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 .

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.