Required Skill -
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2-4 years of experience in software development, data engineering, or backend application development.
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Strong proficiency in Python with hands-on experience in PySpark or Spark-based data processing frameworks.
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Experience designing, developing, or maintaining data pipelines, backend services, and application features.
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Solid understanding of Git-based development workflows, including branching strategies, pull requests, code reviews, and issue tracking.
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Working knowledge of cloud and database technologies such as AWS (S3, IAM, RDS) and PostgreSQL.
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Strong foundation in software testing, including unit testing, integration testing, and data validation practices.
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Experience working within Agile development environments and actively participating in sprint planning, stand-ups, retrospectives, and reviews.
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Ability to translate defined requirements into reliable, high-quality deliverables with minimal supervision.
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Comfortable providing and incorporating constructive feedback during peer code reviews.
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Experience debugging production issues, performing root-cause analysis, and supporting issue resolution.
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Maintains clear technical documentation, including implementation details, assumptions, and key decisions to support knowledge sharing.
Skills
- Exposure to workflow orchestration platforms such as Apache Airflow or similar scheduling tools.
- Domain knowledge or experience in utilities, energy analytics, power systems, or grid-impact analysis.
- Experience working in multi-repository or large-scale engineering environments.
- Understanding of Spark performance optimization and data quality practices for large-scale datasets.
- Exposure to modern data engineering best practices, including observability, monitoring, and data pipeline reliability.
Education:
B.Tech/BE
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