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Senior Data Platfrom Engineer

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Onsite

Senior Data Platfrom Engineer

e-Hireo

Senior Data Platfrom Engineer

Skills

PythonGolangData ScienceKafkaCompliance

1 month ago

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Important information


Contract type:

Permanent

Location:

Bengaluru, Karnataka, India

Work mode:

Onsite

Published on:

24 September 2026

What they need


Experience : 3 - 8 Yrs

Location : Bengaluru

Designation : Senior Data Platfrom 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

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