Python/Pyspark, Bigquery with GCP, Apache Iceberg

Job Description

TCS Hiring !!Virtual Drive *** 12-Nov-25 Python/Pyspark, Bigquery with GCP, Apache Iceberg TCS - Hyderabad 12 PM to 1 PM Immediate Joiners 5 to 7 years Role** Python/Pyspark, Bigquery with GCP, Apache Iceberg Exp - 5 to 7 years Please read Job description before Applying NOTE: If the skills/profile matches and interested, please reply to this email by attaching your latest updated CV and with below few details: Name: Contact Number: Email ID: Highest Qualification in: (Eg. B.Tech/B.E./M.Tech/MCA/M.Sc./MS/BCA/B.Sc./Etc.) Current Organization Name: Total IT Experience- 5 to 7 years Location : Hyderabad Current CTC Expected CTC Notice period: Immediate Whether worked with TCS - Y/N Must-Have** (Ideally should not be more than 3-5) 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 of data engineering best practices. Experience with REST APIs , JSON/XML , and data serialization. Exposure to cloud computing environments. Good-to-Have 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. SN Responsibility of / Expectations from the Role 1 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.