Data Platform & Streaming Engineer

• Design scalable data platform components including lake, lakehouse, data marts, and event streaming systems

• Build real-time and near real-time streaming pipelines using Kafka, Azure Event Hubs, Spark Structured Streaming, and Flink

• Develop ETL/ELT batch pipelines using Spark, SQL, and enterprise orchestration systems such as SAP, SuccessFactors, and engineering platforms

• Implement data modeling approaches including dimensional modeling, Data Vault, and medallion architecture

• Ensure data quality using validation rules, anomaly detection, schema evolution, and automated testing frameworks

• Set up CI/CD pipelines, infrastructure-as-code, and environment deployment automation for data systems

• Implement observability solutions including logging, metrics, tracing, and define SLOs and incident response processes

• Enforce data governance including lineage, cataloging, access control, encryption, retention, and privacy compliance

• Optimize performance and cost through partitioning, caching, job tuning, and streaming backpressure handling

• Support ML workflows including feature stores, training datasets, and online/offline consistency

• Collaborate with cross-functional teams to translate business requirements into scalable data solutions

• Document architectures, standards, and runbooks while mentoring junior engineers