Strong 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 data engineering best practices.

Experience with REST APIs, JSON/XML, and data serialization.

Exposure to cloud computing environments.

5+ years of experience in Python and PySpark development.

Experience with data warehousing and data lakes.

Knowledge of machine learning libraries (e.g., MLlib) is a plus.

Strong problem-solving and debugging skills.

Excellent communication and collaboration abilities.

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 AWS, Azure, or GCP.

Implement data storage solutions and manage large-scale datasets.