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Metro location, popular Data Engineer title, and broad big-data skillset increase applicant competition.
Data engineering skills like Spark, PySpark, and AWS are broadly transferable across industries.
Mandatory Spark/Scala/PySpark, cloud and Airflow expertise plus on-site Pune requirement enforce strict filters.
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Develop and optimize data pipelines processing large-scale structured and unstructured datasets using Spark (Scala/PySpark).
Collaborate with Quest Global teams and customers to design, implement, and troubleshoot data engineering solutions.
Ensure data quality, consistency, and reliability while optimizing performance and resource utilization on cloud platforms, primarily AWS.
Strong hands-on experience with Apache Spark and proficiency in Scala and/or Python (PySpark).
Experience with cloud platforms, preferably AWS (S3, EMR, Glue, Kinesis, Firehose, Hive).
Work Experience Required: Not explicitly mentioned in the JD.
Mandatory to work from Customer Office in Pune location.
Experience with Spark internals, data formats (Parquet, Avro, JSON), and distributed systems/big data concepts.
Familiarity with CI/CD pipelines, streaming frameworks (Spark Streaming), and workflow orchestration tools like Apache Airflow.
Comfortable collaborating in cross-functional teams and owning data quality and reliability in fast-paced environments.