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Tier-1 brand, common Data Engineer role, metro location, and broad skillset requirements drive high competition.
Technical data engineering skills are broadly transferable across industries, so background sensitivity is low.
Mandatory technical stack (PySpark, DBT, Airflow, Databricks) but no explicit years requirement yields medium strictness.
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Design, develop, and deploy scalable data pipelines and workflows using tools like PySpark, Databricks, and Apache Airflow.
Implement and maintain ETL/ELT processes, data models with DBT, and data warehousing solutions (Redshift, Delta Lake, PostgreSQL).
Ensure data quality, optimize query performance, troubleshoot scalability and availability, and coordinate with business and technical teams for automation process flow design.
Bachelor's or Master's degree in Computer Science, Engineering, or MCA.
Experience designing and managing large-scale data pipelines, ETL/ELT processes, and data warehousing solutions.
Proficiency with PySpark, Databricks, Apache Airflow, DBT, and AWS data services (S3, Lambda).
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in building and maintaining robust, scalable data engineering solutions in complex environments.
Skilled at collaborating with data analysts, data scientists, and business stakeholders to translate data requirements into technical solutions.
Proficient in performance optimization, data governance, and troubleshooting within cloud-based data ecosystems.