Big Data Engineer (PySpark / Spark / Kafka)

Job Description: Big Data Engineer (Py. Spark / Spark / Kafka) Work Mode: Hybrid – 3 Days a Week in Office Job Overview We are seeking an experienced Data Engineer with strong expertise in Big Data technologies to design, develop, and support enterprise-scale data platforms. The ideal candidate should have hands-on experience in Py.

Spark, Apache Spark, Kafka, Hadoop ecosystem components, and Apache Ni. Fi, along with a strong understanding of data ingestion, transformation, and real-time processing frameworks.

Key Responsibilities

Design, develop, and optimize scalable data pipelines using Py. Spark, Spark, Hadoop, and Apache Ni. Fi. Build and maintain batch and real-time data processing solutions. Develop and support Kafka-based streaming applications and event-driven architectures. Create and optimize ETL/ELT workflows for large-scale structured and unstructured datasets.

Develop complex SQL queries for data extraction, transformation, validation, and troubleshooting. Implement data ingestion solutions from databases, APIs, files, and streaming sources. Monitor, troubleshoot, and enhance the performance of Spark jobs and data pipelines. Collaborate with architects, business analysts, and development teams to deliver high-quality data solutions.

Support platform upgrades, deployments, testing, certification, and production releases. Ensure data quality, governance, security, and operational excellence across data platforms. Mandatory Skills Py. Spark Apache Spark (Spark SQL, Data. Frames) Apache Kafka Hadoop Ecosystem (HDFS, Hive, YARN) Apache Ni. Fi SQL Python Preferred Skills Spark Streaming Airflow / Oozie Hive Scala Jenkins, Bitbucket, Git JIRA, Confluence Cloud Platforms (GCP/AWS/Azure) Data Warehousing Concepts and Dimensional Modeling