Tata Consultancy Services
Walk-in || Pyspark Data Engineer
Skills
2 months ago
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Important information
Contract type:
Permanent
Location:
Hyderabad, Telangana, India
Work mode:
Onsite
Published on:
9 September 2026
What they need
TCS WALK-IN DRIVE!!!!
Walk-In Locations: Hyderabad,Bengaluru, Kolkata
Mode: Physical Walk-In drive In-Person (Face to Face)
Walk-In Drive Date: 19 Sep 26 (sat)
Reporting Time: 9.00 AM to 1:00 PM
Skill - Pyspark Data Engineer
Experience - 4 to 10 years
Responsibilities:
- Data Processing: Design, develop, and maintain data processing solutions using PySpark, Apache Spark, and Python.
- Data Pipeline Development: Develop and optimize data pipelines using PySpark, Apache Spark, and cloud-based data platforms.
- Data Integration: Integrate data from various sources, including relational databases, NoSQL databases, and cloud storage.
- Data Transformation: Develop and implement data transformation logic using PySpark, Apache Spark, and Python.
- Collaboration: Work with cross-functional teams to identify and prioritize project requirements, provide technical guidance, and ensure data quality.
Required Skills:
- PySpark: In-depth knowledge of PySpark, Apache Spark, and Python.
- Data Processing: Strong understanding of data processing concepts, including data ingestion, data transformation, and data storage.
- Cloud Experience: Experience with cloud-based data platforms, including AWS, Azure, or Google Cloud.
- Expertise in DataFrames & Spark SQL, Spark Streaming with Apache Kafka real-time data ingestion pipelines
- Very good conceptual understanding of Multithreading, distributed computing concepts of Pyspark
- Programming: Proficiency in programming languages, including Python, Java, or Scala.
- Communication: Excellent communication and collaboration skills.
Preferred Skills:
- Apache Spark Certifications: Relevant certifications, such as Apache Spark Certification or Cloudera Certified Spark Developer.
- Big Data: Experience with big data technologies, including Hadoop, HBase, or Cassandra.
- Machine Learning: Experience with machine learning frameworks, including TensorFlow, PyTorch, or Scikit-learn.
- Data Science: Experience with data science tools, including Jupyter Notebook, Pandas, or NumPy
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