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Tier-1 brand, metro role, generalist data-engineer title, and broad in-demand stack drive high competition.
Cloud, Snowflake, Spark, and Python skills are highly transferable across industries.
Explicit 7+ years and mandatory Snowflake/AWS/Python/Spark and governance requirements create strict shortlisting.
Design, build, and optimize scalable data pipelines and platforms using AWS, Snowflake, Python, and Apache Spark to support advanced analytics and AI initiatives.
Implement data quality, governance, and metadata management frameworks ensuring compliance, security, and operational efficiency.
Collaborate with stakeholders across product, architecture, and platform teams while mentoring engineers and promoting engineering best practices.
7+ years experience building modern data solutions with Snowflake, SQL, Python, Apache Spark, and cloud technologies (AWS preferred).
Hands-on experience with AWS services including Glue, S3, Lambda, Step Functions, and workflow orchestration.
Proven expertise in ETL/ELT processes, data modeling, orchestration, performance optimization, and data governance including metadata management and lineage.
Work Experience Required: Minimum 7 years building data solutions; Notice period: Not explicitly mentioned in the JD.
Experienced in delivering enterprise-grade data platforms enabling AI/ML and advanced analytics in cloud-native environments.
Capable of implementing and enforcing data governance and compliance standards within complex data ecosystems.
Skilled at mentoring engineering teams and driving adoption of software engineering best practices like CI/CD, automated testing, and version control.