Big Data Engineer_STG

**Role & responsibilities** **•** Design, develop, and maintain scalable batch and streaming data pipelines using **Spark** and **Scala**. **•** Build and optimize data processing workflows leveraging **Hive** for querying, transformations, and data warehousing needs. **•** Implement reliable ingestion and integration patterns for high-volume datasets, ensuring data quality, consistency, and completeness. **•** Develop reusable Spark jobs, libraries, and frameworks to standardize data engineering practices across teams. **•** Tune Spark applications for performance (partitioning, caching, shuffles, memory management) and improve runtime efficiency. **•** Work with stakeholders to understand data requirements and deliver well-modeled datasets for downstream consumption. **•** Implement monitoring, alerting, and operational runbooks to ensure pipeline reliability and faster incident resolution. **•** Perform code reviews, enforce engineering best practices, and contribute to continuous improvement of data platform standards. **Preferred Qualifications:** **•** Hands-on experience with **Kafka** for building streaming ingestion and event-driven data pipelines. **•** Experience designing end-to-end data architectures (ingestion, processing, storage, and serving layers) for large-scale systems. **•** Strong understanding of data partitioning strategies, file formats, and efficient processing patterns for big data workloads. **•** Proven ability to lead technical discussions, mentor engineers, and drive best practices across delivery teams. **•** Experience improving reliability through automated validations, data quality checks, and operational excellence practices. **Good to have skills:** Hadoop, HDFS, YARN, Airflow, HBase **Location**: PAN INDIA **EXP**:8-15 Years **Preferred candidate profile**