Streaming Data Platform Engineer

We are seeking an experienced software engineer to help design and build large-scale, cloud-native data platform capabilities with a strong emphasis on real-time streaming. This is a platform engineering role, not a conventional ETL or analytics development position. The engineer will create reusable services, SDKs, frameworks, and self-service capabilities that enable internal consumers - including machine learning practitioners - to process, manage, and access high-volume data reliably., * Design, build, and operate distributed streaming systems using Apache Flink and/or Apache Spark Structured Streaming for high-throughput, low-latency workloads.

• Develop production-grade platform services, APIs, reusable frameworks, and SDKs using Python and/or Java (Spring Boot).
• Contribute to control-plane and data-plane architecture, including workload orchestration, configuration, lifecycle management, and operational reliability.
• Enable scalable data management for ML consumers: dataset organization, efficient access and retrieval, indexing patterns, and repeatable training-data workflows.
• Engineer observability, fault tolerance, checkpointing, recovery, scaling, performance optimization, and automated testing for distributed processing systems.
• Partner with platform teams, application engineers, and ML practitioners to translate use cases into self-service capabilities and well-supported developer interfaces.
• Apply security and governance practices, including authentication, authorization, data protection, and appropriate handling of sensitive datasets.

• Strong hands-on real-time streaming experience with Apache Flink or Apache Spark Structured Streaming in production; able to explain design tradeoffs and incidents.
• Deep understanding of distributed data processing, partitioning, state management, event-time processing, backpressure, and failure recovery.
• Strong software engineering proficiency in Python or Java, including API/service design, automated tests, and maintainable code.
• Experience building data platform capabilities, internal developer platforms, reusable libraries/SDKs, or large-scale backend systems - beyond individual pipelines.
• Experience with microservices, containers, cloud-native deployment, CI/CD, monitoring, and production operations.
• Ability to work across requirements ambiguity, evaluate architecture options, and communicate tradeoffs with engineering stakeholders.
• Hands-on knowledge of both Flink and Spark, plus Databricks or comparable managed distributed-data platforms.
• Experience with Java/Spring Boot and Python in the same platform ecosystem.
• Experience supporting ML practitioners through training-data access, feature/dataset management, data quality, indexing, or governed data interfaces.
• Familiarity with Kafka or comparable event-streaming infrastructure; schema evolution and exactly-once/at-least-once delivery tradeoffs.
• Experience with Kubernetes, service orchestration, cloud object storage, data access governance, and RBAC/ABAC patterns.

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