[Responsibilities] Design and implement real-time and batch data pipelines Build and maintain scalable streaming systems Develop and optimize stream processing jobs Ensure reliable ingestion from multiple internal and external data sources Design event schemas and data contracts Implement data validation, transformation, and enrichment logic Optimize storage layouts and lifecycle management strategies Improve system observability (metrics, logging, alerting) Troubleshoot and resolve performance bottlenecks in distributed systems Implement retry, dead-letter, and replay mechanisms Ensure data quality, consistency, and governance Collaborate with Backend, DevOps, and Security teams [Requirements] Must-Have Proficiency in Java, Python, and SQL; strong software engineering fundamentals Experience with distributed messaging systems (e.g., Apache Kafka) and stream processing frameworks (e.g., Apache Flink) Knowledge of event-time processing, windowing, state management, and handling out-of-order events Experience with cloud storage/data lakes, relational databases, and search/indexing engines (e.g., OpenSearch / Elasticsearch) Familiarity with cloud platforms (AWS preferred), IaC (Terraform), CI/CD, and containerization (Docker) Strong problem-solving skills and ability to design scalable, fault-tolerant data pipelines Nice-to-Have Experience with workflow orchestration platforms Experience in security, log processing, or observability domains Schema management tools (Avro, Protobuf, Schema Registry) Data lake table formats (Iceberg, Hudi, Delta Lake) Experience with distributed query engines (Athena, Trino, Presto) Multi-tenant system design Cost optimization in large-scale cloud environments Soft Skills Strong problem-solving and debugging skills in distributed systems Ownership mindset with attention to reliability and quality Clear communication and documentation skills Ability to work cross-functionally Comfort working in fast-paced environments Qualifications Bachelor’s degree in Computer Science or equivalent experience. 7+ years of experience in data engineering or distributed systems Strong fundamentals in system design and scalability Proven experience operating production-grade data platforms Ability to balance performance, cost, and reliability