About this role
Design, develop, and implement scalable, resilient, and high-performance data pipelines and data architecturesfor enterprise data platforms.
· Build and optimize ETL/ELT pipelines supporting batch, near-real-time, and streaming data processing requirements.
· Design and implement enterprise data ingestion and integration workflows using Apache NiFi, including data routing, transformation, orchestration, monitoring, and integration across heterogeneous data sources and platforms.
· Develop distributed data processing solutions using Apache Spark with Scala and/or Python (PySpark).
· Design and implement data solutions across relational database management systems (RDBMS) and modern data lakehouse technologies such as Apache Iceberg.
· Build and optimize federated and distributed SQL capabilities using Starburst and Trino query engines.
· Develop event-driven and real-time data processing solutions using Apache Kafka and Spark Streaming / Structured Streaming.
· Lead end-to-end delivery of enterprise-scale data engineering initiatives involving multiple engineering, product, architecture, and business teams.
· Strengthen platform reliability through automation, CI/CD, testing, monitoring, observability, documentation, and operational excellence.
· Improve data quality, performance, scalability, resiliency, and cost efficiency across data platforms and pipelines.
· Support enterprise data governance by maintaining metadata, technical and business documentation, data lineage, data quality controls, and secure access to data assets.
· Partner with security and governance teams to ensure data solutions meet enterprise requirements for privacy, security, access control, compliance, and data lifecycle management.
· Troubleshoot complex production issues, perform root-cause analysis, and implement sustainable solutions to improve platform stability.
· Collaborate effectively with stakeholders across engineering, architecture, analytics, AI/ML, governance, product, and business organizations.