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Tier-1 brand and metro location increase competition, though senior specialization reduces applicant density.
Core data engineering skills (Python, PySpark, cloud, ETL) are highly transferable across industries.
Explicit 8–13 years plus required cloud, Python, PySpark, SQL, and big-data skills raises shortlisting strictness.
Design, build, and optimize scalable data pipelines and platforms using cloud platforms (AWS preferred).
Own data pipeline projects including scope, timelines, risks, and deployment.
Ensure data quality through rigorous testing and monitoring; collaborate with analysts, scientists, and stakeholders to meet data requirements.
8 to 13 years of experience in data engineering.
Bachelor’s degree in computer science, engineering preferred; other engineering fields considered.
Hands-on experience with cloud platforms (AWS, Azure, GCP), Python, PySpark, and SQL.
Experience with big data ETL performance tuning.
Experienced in managing end-to-end data pipeline projects with strong ownership and operational accountability.
Skilled in cloud architecture for cost-effective, scalable data solutions, preferably with AWS.
Familiarity with software engineering best practices (version control, CI/CD, automated testing) and mentoring junior engineers.