Python/Pyspark, Bigquery with GCP, Apache Iceberg

7 days ago

New Delhi, India Tata Consultancy Services Full time

TCS Hiring Virtual Drive ***12-Nov-25 Python/Pyspark, Bigquery with GCP, Apache IcebergTCS - Hyderabad12 PM to 1 PMImmediate Joiners5 to 7 yearsRole** Python/Pyspark, Bigquery with GCP, Apache IcebergExp -5 to 7 yearsPlease read Job description before ApplyingNOTE: 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 yearsLocation : HyderabadCurrent CTCExpected CTCNotice period: ImmediateWhether worked with TCS - Y/NMust-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.SNResponsibility of / Expectations from the Role1- 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.

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