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Python, Pyspark, GCP_Virtual drive in Week days

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Onsite

Python, Pyspark, GCP_Virtual drive in Week days

Tata Consultancy Services

Python, Pyspark, GCP_Virtual drive in Week days

Skills

Google Cloud PlatformSQL QueriesBig DataData EngineeringPythonKafkaMachine LearningAWSPySparkHadoopAzure

1 day ago

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Important information


Contract type:

Permanent

Salary:

₹700,000 – ₹1,700,000 / year

Location:

Hyderabad, Telangana, India

Work mode:

Onsite

Published on:

24 October 2026

What they need


Role: Python, Pyspark ,GCP(Data engineer)

Experience : 6 to 10 Yrs

Location : Hyderabad

Role & responsibilities

Desired Competencies (Technical/Behavioral Competency)

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 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.

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