Senior Data Platform Engineer

Job Description :

- Architect and drive large-scale distributed systems, data platforms, and backend services that are highly available, cost-efficient, secure, and extensible.

- Establish platform vision and evolve foundational frameworks with multi-tenant, self-serve, and platform-first thinking.

- Design for scale and resilience - enforcing clean architecture, observability, fault tolerance, extensibility, and performance.

- Lead end-to-end initiatives from problem definition to production rollout, balancing business impact with long-term technical sustainability.

- Mentor and grow engineers (including Senior Engineers), setting technical direction and raising the quality bar.

- Make deep technical trade-offs explicit - connecting system internals, architectural decisions, and platform costs.

- Champion best practices in code quality, instrumentation, design patterns, and cloud-native development.

- Influence cross-team architecture by partnering with product, infra, and data science.

- Drive innovation by identifying gaps, proposing bold solutions, and creating reusable building blocks.

- Demonstrate thought leadership by anticipating scale challenges and ensuring security/compliance.

Must Haves:

- Deep expertise in modern data lake/warehouse and distributed data architectures - hands-on with Trino, Presto, Iceberg/Delta Lake, Spark, Kafka, and large-scale storage/compute separation.

- Coding proficiency in Golang or Python.

- Proven track record building and scaling multi-tenant data platforms, with awareness of cost efficiency, security, compliance, and operational excellence.

- Architectural intuition at scale - evaluating trade-offs between performance, latency, fault-tolerance, extensibility, and cost.

- Strong system internals knowledge (compilers, query engines, storage formats, distributed consensus, networking, JVM internals, or equivalent).

- Hands-on leadership - mentoring senior engineers and driving adoption of engineering best practices.

Skills

DataLake, Spark, Kafka, Python, Distributed Architecture, Golang, JVM, Data Science, Data Observability