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Tier-1 employer, common data-engineer skillset, and metro location drive high candidate density.
Core data-engineering skills are broadly transferable, though enterprise banking and Teradata experience add domain specificity.
Specific enterprise data stack requirements (Snowflake, Teradata, PySpark) increase technical filtering despite no explicit years.
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Build and maintain data pipelines, data warehouses, and data lakes to ensure accurate, accessible, and secure data.
Design and implement scalable data architectures managing data volume, velocity, and security adherence.
Collaborate with data scientists to develop and deploy machine learning models integrated with data infrastructure.
Advanced SQL query writing and performance tuning skills.
Experience with Teradata for enterprise data warehousing.
Proficiency in PySpark and familiarity with Hadoop ecosystems.
Work Experience Required: Not explicitly mentioned in the JD; Role location: Pune, India.
Experienced in building and scaling complex data architectures handling large data volumes with security compliance.
Strong technical knowledge in distributed data processing and automation via Linux Shell Scripting.
Familiarity or prior exposure to AWS cloud data platforms preferred to support digital transformation initiatives.