Senior Platform Engineer

About the Role

As part of the Data Platform team, you'll build large-scale backend services and distributed data systems powering KreditBee's business operations and intelligence working on high-throughput, low-latency infrastructure that processes billions of events and transactions with high reliability. You'll collaborate closely with senior engineers to deliver well-architected, observable, resilient, and scalable components.

What We Expect From You

• 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

Mandate Skills:

• Python
• Snowflake
• Trino
• Iceberg
• Golang