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Tier‑1 brand, popular data-engineer title, metro locations, and broad cloud-spark skillset increase applicant competition.
Core cloud data engineering skills are transferable, though SAS and regulated-compliance experience raise domain specificity.
Many mandatory cloud, PySpark, SAS migration, and orchestration skills increase technical filter rigidity.
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Design, develop, and maintain scalable ETL/ELT data pipelines and data architecture within AWS ecosystem using PySpark and AWS services.
Execute complex data migrations from legacy SAS systems to modern cloud architectures ensuring data integrity and optimized performance.
Collaborate with cross-functional teams to deploy cloud-native data solutions focusing on security, reliability, and cost-efficiency.
Experience with PySpark and AWS services including Glue, EMR, S3, Redshift.
Experience in data migration and handling legacy SAS environments.
B.Tech/BE in Computer Science or Information Technology.
Work Model: Hybrid with mandatory 3 days onsite; Location: Any location in India (preferably Pune).
Strong expertise in AWS cloud data services and distributed computing frameworks (PySpark).
Proven experience in migrating and integrating legacy SAS data systems to cloud-native architectures.
Familiarity with DevOps, CI/CD pipelines, and workflow orchestration tools like Apache Airflow or AWS Step Functions in a regulated environment.