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Tier-1 brand, generic software title, broad platform requirements, and likely metro hiring raise competition.
Requires deep data-platform and distributed-systems expertise, limiting cross-industry transfer without domain experience.
Explicit seniority plus mandatory distributed-systems, databases, streaming, cloud, and language requirements enforce strict filtering.
Own end-to-end data platform including relational, columnar, object storage, caches, and queues, ensuring correctness, performance, cost-efficiency, and tenant isolation at production scale.
Design, build, and operate streaming and compute pipelines that move and transform data with focus on latency, indexing, sharding, and hot-path optimization.
Develop and maintain performance tooling, capacity models, data operations, and observability instrumentation to guide scale, cost, and reliability decisions.
Bachelor's degree with 7+ years related experience or Master's with 4+ years or PhD with 1+ year in Computer Science or related field.
Strong backend engineering experience with scalable, reliable, production-grade data-intensive services and distributed systems.
Hands-on experience with OLTP databases (e.g., PostgreSQL, MySQL) and analytical/columnar stores (e.g., ClickHouse, Druid), including schema design and query tuning.
Proficiency in Python plus at least one backend language (Go, Java, C++), plus experience with streaming/queueing systems (Kafka, RabbitMQ) and performance engineering (profiling, benchmarking, load testing).
Experienced in building and scaling complex data infrastructures at production scale with strong ownership of performance, capacity, and cost metrics.
Comfortable working independently on architectural design, debugging, and mentorship in a cloud-native environment with Kubernetes and public cloud platforms.
Skilled in advanced distributed systems concepts, multi-tenant isolation, and delivering end-to-end operational reliability for data pipelines and storage.