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Tier-1 brand plus a mid-level, widely sought data engineering role increases applicant competition.
Data engineering skills (ETL, PySpark, cloud) are moderately transferable across industries.
Explicit 2–5 years plus mandatory Databricks, PySpark and AWS skills make filters stringent.
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Design, develop, and maintain scalable and reliable data pipelines and infrastructure to support business intelligence and analytics.
Ensure data quality, accessibility, and service reliability by deploying secure, well-tested software complying with privacy and compliance requirements.
Participate in on-call rotations and site-reliability engineering practices, improve CI/CD pipelines and developer velocity, and collaborate with cross-disciplinary teams including data scientists and software engineers.
2 to 5 years of hands-on experience building and maintaining data infrastructure and products in complex environments.
Proficiency in object-oriented programming languages such as Python, Scala, Java, or C#.
Strong experience with Databricks, Delta Lake, PySpark, and AWS services like Glue, Lambda, and Redshift.
Bachelor's degree in computer science or related field.
Experienced in implementing large-scale distributed data systems in collaboration with senior team members.
Skilled in software engineering best practices including technical design, code review, unit testing, and monitoring.
Comfortable working within a cloud environment with expertise in end-to-end data lifecycle technologies.