Position: Data Engineer

Experience: 3+ Years

Joining Preference: Immediate / Early Joiners only

About The Role
We are looking for a skilled Data Engineer with strong hands-on experience in Python, SQL, PySpark, and cloud data platforms (AWS/Azure). The ideal candidate will have experience designing and developing scalable ETL/ELT pipelines, processing large datasets, and building reliable data solutions for analytics and reporting.

Key Responsibilities
- Design, develop, and maintain scalable ETL/ELT data pipelines.
- Develop data processing solutions using Python and PySpark.
- Write and optimize complex SQL queries, stored procedures, and database objects.
- Build and maintain cloud-based data pipelines using AWS or Azure data services.
- Ingest, transform, and integrate structured and semi-structured data from multiple data sources.
- Perform data transformation, cleansing, validation, and integration.
- Build and maintain efficient data models for analytics and reporting.
- Monitor data pipelines and troubleshoot performance, reliability, and data quality issues.




- Optimize data processing jobs for scalability and performance.
- Collaborate with Data Analysts, BI developers, Software Engineers, and other cross-functional teams.
- Ensure data quality, reliability, security, and governance across data platforms.
- Follow best practices for code quality, testing, documentation, version control, and CI/CD.

Required Skills
- Strong programming skills in Python.
- Strong hands-on experience with SQL, including joins, CTEs, window functions, query optimization, and performance tuning.
- Hands-on experience with PySpark / Apache Spark for large-scale data processing.
- Robust understanding of ETL/ELT concepts and data integration.
- Experience working with cloud platforms such as AWS or Azure.
- Experience with cloud-based data engineering, ETL, and big data technologies.
- Good understanding of data warehousing and data modeling concepts.
- Experience working with

📌 Data Engineer (Pune Division)
🏢 Setoo
📍 Pune Division