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Tier-1 employer, popular Data Engineer title, metro location and broad skillset increase candidate competition.
Core data engineering skills (PySpark, SQL, cloud) are highly transferable across industries.
Explicit 8–13 years requirement plus mandatory cloud, Python, PySpark and ETL skills make filters stringent.
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Design, build, and optimize scalable data pipelines and ETL/ELT processes, owning projects end-to-end including scope, timelines, and risks.
Ensure data quality and integrity through testing and monitoring while collaborating with data analysts, scientists, and business stakeholders to meet data requirements.
Leverage cloud platforms (AWS preferred) to architect and implement efficient data solutions, while mentoring junior engineers and resolving complex data challenges.
8 to 13 years of relevant work experience in data engineering or related fields.
Bachelor's degree in Computer Science, Engineering, or related field is preferred.
Strong hands-on experience with cloud platforms (AWS, Azure, or GCP), Python, PySpark, SQL, and big data ETL performance tuning.
Notice period: Not explicitly mentioned in the JD.
Experienced in architecting cost-effective, scalable data solutions primarily on AWS cloud platform.
Skilled in end-to-end ownership of data pipeline projects demonstrating strong problem-solving and analytical capabilities.
Familiar with data engineering best practices including version control, CI/CD, automated testing, and working with global cross-functional teams.