DE&A -Data Engineering Apache

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.

7+ years of experience in Data Engineering, Software Engineering, Enterprise Data Platforms, or a related technical discipline.

• Strong hands-on experience designing and delivering enterprise-scale data pipelines and data architectures.
• Strong understanding of ETL and ELT patterns, data integration, data modeling, and distributed data processing.
• Hands-on development experience with Apache Spark using Scala and/or Python/PySpark.
• Experience working with RDBMS technologies and modern lakehouse/table formats, preferably Apache Iceberg.
• Experience with distributed SQL/query technologies such as Starburst and Trino.
• Experience designing real-time and streaming data solutions using technologies such as Apache Kafka and Spark Streaming.
• Understanding of integration patterns across NiFi, Kafka, Spark, RDBMS, and lakehouse platforms for batch and real-time data processing.
• Strong knowledge of software engineering practices including CI/CD, automated testing, source control, infrastructure/platform automation, observability, and production support.
• Experience with data governance, metadata management, data lineage, data quality, and secure data access in enterprise environments.
• Proven ability to lead end-to-end delivery of enterprise-scale data engineering initiatives across multiple teams.
• Ability to lead technical discussions, influence architectural decisions, establish engineering standards, and mentor engineers.
• Excellent problem-solving, communication, stakeholder management, and cross-functional collaboration skills.
• Ability to translate complex business and technical requirements into scalable, maintainable, and production-ready data engineering solutions.
• Demonstrated ownership mindset with a strong focus on engineering quality, platform reliability, security, scalability, and operational excellence.

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