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.

Responsibilities

 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.

Qualifications

NA

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