Key Responsibilities
Develop and maintain data pipelines using PySpark
Process and analyze large-scale datasets in distributed environments
Design and implement ETL/ELT workflows
Optimize Spark jobs for performance and scalability
Work with data stored in HDFS, Hive, or cloud storage (S3, ADLS)
Collaborate with data engineers, analysts, and business teams
Ensure data quality, integrity, and governance
Debug and troubleshoot data processing issues
Automate workflows using scheduling tools (Airflow, Oozie, etc.)
Write clean, scalable, and efficient code
Required Skills & Qualifications
Technical Skills
Strong proficiency in Python and PySpark
Good experience with Apache Spark (RDDs, DataFrames, Spark SQL)
Knowledge of Hadoop ecosystem (HDFS, Hive)
Experience in ETL pipeline development
Familiarity with SQL and database concepts
Experience with data formats (Parquet, ORC, JSON, CSV)
Basic understanding of distributed computing concepts
Exposure to version control tools (Git)

Preferred Skills (Nice-to-Have)
Experience with cloud platforms (AWS, Azure, GCP)
Knowledge of Databricks or EMR environments
Familiarity with workflow orchestration tools (Airflow)
Exposure to Kafka or real-time data streaming
Understanding of Delta Lake / Lakehouse architecture
Experience with NoSQL databases (MongoDB, Cassandra)
Knowledge of CI/CD pipelines and DevOps practices
Basic understanding of machine learning workflows

* Primary skills: Pyspark