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Tier-1 brand, metro location, popular data/ETL role, and mid-level experience increase candidate competition.
Core data engineering skills transfer across industries, though utilities/energy domain preference raises moderate sensitivity.
Explicit 2-4 years plus mandatory PySpark/Python, cloud, DB, and testing skills make filters strict.
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Develop, design, and maintain data pipelines and backend services primarily using Python and PySpark.
Engage in code reviews and peer feedback, maintain clear technical documentation, and support debugging and root cause analysis for production issues.
Operate within Agile teams, actively participating in sprint activities, ensuring delivery of tested and high-quality software components.
2-4 years of experience in software development, data engineering, or backend application development.
Proficiency in Python with hands-on experience in PySpark or Spark-based data processing.
Working knowledge of AWS services (S3, IAM, RDS) and PostgreSQL databases.
Education: B.Tech/BE.
Experience with workflow orchestration tools like Apache Airflow or similar scheduling platforms.
Familiarity with utilities, energy analytics, power systems, or grid-impact analysis domains.
Comfortable working in multi-repository, large-scale engineering environments with an emphasis on Spark optimization and large-scale data quality.