- 4 to 8 + Years of experience using Python and Pyspark.
- Robust proficiency in Python programming.
- Hands-on experience with PySpark and Apache Spark.
- Knowledge of Big Data technologies (Hadoop, Hive, Kafka, etc.).
- Experience with SQL and relational/non-relational databases.
- Familiarity with distributed computing and parallel processing.
- Understanding of data engineering best practices.
- Experience with REST APIs, JSON/XML, and data serialization.
- Exposure to GCP services and cloud computing environments.
- Develop and maintain scalable data pipelines using Python and PySpark.
- Design and implement ETL (Extract, Transform, Load) processes.
- Optimize and troubleshoot existing PySpark applications for performance.
- Collaborate with cross-functional teams to understand data requirements.
- Write clean, efficient, and well-documented code.
- Conduct code reviews and participate in design discussions.
- Ensure data integrity and quality across the data lifecycle.
- Integrate with cloud platforms like GCP, AWS or Azure.
- Implement data storage solutions and manage large-scale datasets.