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Remote, popular Data Engineer role with broad AWS/Spark/Kafka requirements in a Gurgaon metro increases competition.
Core data engineering skills (AWS, Spark, Python, SQL) are highly transferable across industries.
Multiple mandatory AWS, Spark, Python, SQL and DevOps skills create stringent technical screening.
Own development and maintenance of scalable large-scale data pipelines handling multi-source large datasets with batch and real-time processing.
Optimize performance and ensure scalability, reliability, and cost efficiency of data processing jobs and overall technology solutions on AWS cloud.
Collaborate with cross-functional teams including DevOps, data scientists, analysts, and business stakeholders to support data-driven decision-making and advanced analytics.
Hands-on experience with AWS data services (S3, Lambda, EMR, Glue, Redshift, RDS/Postgres, Athena).
Proficiency in Python, PySpark, SQL/PLSQL for data pipeline development and ETL processes.
Experience with distributed computing platforms such as Spark and Kafka for batch and real-time data processing.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in cloud-native data engineering with expertise in AWS ecosystem and modern data stack including data modeling and data warehousing.
Operational focus on performance optimization, cost control, and delivery of scalable data infrastructure.
Collaborative working style interfacing with business users, DevOps, and analytics teams to align data solutions with business objectives.