Python,Pyspark,GCP

Tata Consultancy Services; Hyderabad; On-site; INR 2,500,000 - 3,600,000; Full time

Job summary

Tata Consultancy Services is seeking a seasoned Data Engineer to design and maintain scalable data pipelines using Python and PySpark. The role emphasizes hands-on development, ETL processes, and performance optimization across distributed data environments.

Responsibilities include collaborating with cross-functional teams, ensuring data quality, and implementing best practices for reliability and scalability in cloud-enabled data architectures.

Qualifications

• 6–8+ years of experience with Python and PySpark.
• 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.

Responsibilities

• Develop and maintain scalable data pipelines using Python and PySpark.
• Design and implement ETL (Extract, Transform, Load) processes.
• Optimize and troubleshoot 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.

Skills

Python

PySpark

Apache Spark

Big Data

SQL

REST APIs

JSON/XML

Distributed computing

ETL

Data pipelines

GCP

AWS

Azure

Hadoop

Hive

Kafka

Code reviews

Job description

Role & responsibilities

• 6 to 8 + Years of experience using Python and Pyspark.
• 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 GCP services and cloud computing environments.
• 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 GCP, AWS or Azure.
• Implement data storage solutions and manage large-scale datasets.