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Tier-1 employer, mid-level generalist backend role, and hybrid metro posting increase competition.
Requires deep data-platform, distributed-systems, and performance expertise, limiting cross-industry transferability.
Explicit years plus mandatory large-scale DB, streaming, cloud, and performance expertise tightens candidate filters.
Own and evolve the full data plane end-to-end for the platform, including relational, analytical/columnar, object storage, caches, queues, streaming, compute pipelines, and performance tooling at production scale.
Ensure data platform correctness, speed, cost-efficiency, and tenant isolation, managing capacity and performance models with quotas and rate limiting.
Collaborate closely with product engineering teams; handle production software design, debugging, testing, infrastructure operations, performance monitoring, and mentoring technical peers.
Bachelor's degree with 7+ years experience or Master's with 4+ years or PhD with 1+ year in Computer Science or related field.
Strong backend software engineering experience building and operating scalable, reliable, production-grade data-intensive services and platforms in Python and at least one other backend language (Go/Java/C++/similar).
Hands-on experience with OLTP databases (e.g., PostgreSQL, MySQL) and analytical/columnar/time-series stores (e.g., ClickHouse, Druid, BigQuery), including schema design and query tuning.
Experience with streaming or queueing systems (e.g., Kafka, RabbitMQ), streaming data processing compute, performance engineering (profiling, benchmarking, load testing), and cloud-native/containerized environments (Kubernetes, AWS/GCP).
Experienced in designing, tuning, and operating large-scale distributed data platforms focusing on multi-tenant isolation, capacity modeling, and cost efficiency.
Strong background in performance engineering with ability to translate metrics into actionable capacity and cost decisions.
Comfortable with full lifecycle ownership including designing, debugging complex issues, building observability tooling, and mentoring engineers in a cloud-native production environment.