Required Experience

- 3-5 years of hands-on experience in data engineering, with a strong track record of delivering production grade data pipelines.
- Experience in the banking or financial services domain is highly preferred.
- Experience implementing data solutions on cloud-native platforms is an advantage.

Technical Skills

- Robust hands-on proficiency in PySpark for large-scale data processing on Cloudera Data Platform (CDP).
- Experience with distributed data processing frameworks on CDP.
- Solid understanding in PySpark Coding, debugging and Productionization skills.
- Exposure to AI Assisted Coding tools like Claude Code is a huge advantage.
- Solid understanding of Data Vault 2.0 and dimensional modelling techniques.
- Familiarity with data warehousing concepts,



Big Data architectures/ components and technologies (Hive, Spark, Oozie, Airflow, etc.) is a must.
- Experience working with CI/CD pipelines and version control (e.g., Git, Jenkins).
- Knowledge of data quality frameworks, metadata management, and data governance practices.
- Proficiency in Exploratory Data Analysis (EDA) to understand data characteristics and inform pipeline design.
- Exposure to ETL and BI reporting tools such as Microsoft Power BI is an advantage.
- Experience with Feature engineering for analytical or ML use cases is an added advantage.
- Familiarity with GitHub Actions for pipeline automation is a plus

📌 Pyspark Data Engineer (Chennai)
🏢 Clover Infotech
📍 Chennai