Ebay MTS Software Engineer Data Platforms India, Karnataka, Bengaluru 581014241
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We’re seeking a Member of Technical Staff 1 (Software Engineer) who can work independently and is an expert in distributed systems . You’ll design and deliver well-scoped services and features that advance eBay’s Core Data Platform—improving scalability, reliability, and developer experience. This role is Data Platform Engineering not data engineering ): you’ll build and evolve the platform itself rather than author application pipelines.
What you’ll do and learn
Independently design, implement, and ship distributed services and features end-to-end (design → code → tests → deploy → operate).
Build core platform capabilities across ingestion, streaming, lakehouse/warehouse, catalog, and governance .
Write production-grade code with strong observability (metrics, logs, traces) and SLOs , and participate in on-call for the services you own.
Diagnose and resolve performance, scalability, and correctness issues in distributed environments.
Contribute design docs for your areas; participate in reviews to uphold reliability, security, and cost best practices.
Collaborate with product, infra, and analytics teams to align technical work with business outcomes.
6+ years of professional software engineering experience (or equivalent impact).
Expertise in distributed systems fundamentals (consensus, replication, partitioning, consistency models, fault tolerance) and practical experience building and running such systems in production.
Strong coding skills in Java/Python and familiarity with CI/CD .
Hands-on with some of: Kafka/Flink , Spark , Delta/Iceberg , Kubernetes , NoSQL/columnar stores .
Proven ability to work independently , make sound tradeoffs, and deliver quality outcomes with minimal supervision.
Solid debugging, performance analysis, and system design skills.
multi-tenant platform services, data governance, or privacy-by-design controls.
Contributions to open-source distributed systems or data platforms.
ships independent, well-scoped features/services to production with strong reliability.
Demonstrably improves throughput/latency/cost or availability/SLOs on owned services.
Becomes a go-to engineer for distributed-systems debugging and design conversations within the team.
Maintains high code quality, test coverage, and quality-in-release metrics.
Impact at scale: Your platform work powers analytics and ML across a global marketplace.
Hard problems: Streaming freshness/correctness, storage/compute efficiency, multi-region resiliency.
Collaborative culture: Inclusive team that values autonomy, craftsmanship, and knowledge sharing.
Growth:
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