Key Responsibilities
Design, develop, and maintain scalable data pipelines using Python, Hadoop, and Apache Spark.
Develop productive data processing and transformation workflows for large datasets.
Write, optimize, and maintain complex SQL queries and PL/SQL procedures.
Perform data extraction, transformation, and loading (ETL) from multiple data sources.
Work with structured and unstructured data to support analytics and reporting requirements.
Optimize data pipelines and queries for performance, scalability, and reliability.
Ensure data quality, consistency, security, and integrity across data platforms.
Collaborate with cross-functional teams to understand business requirements and deliver data engineering solutions.
Use Git, automation, and CI/CD practices to support development and deployment.





Required Skills
4.5–10 years of experience in software development or data engineering.
Robust proficiency in AWS
Hands-on experience with Hadoop and Apache Spark.
Valuable knowledge of SQL and PL/SQL, including query optimization and stored procedures.
Experience in ETL development, data processing, and data pipeline design.
Understanding of data warehousing concepts and distributed computing.
Strong problem-solving, debugging, and performance-tuning skills.

Skills:- Data engineering, Python, Spark, Amazon Web Services (AWS) and PySpark

📌 Data Engineer Gurugram
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