5 - 10 years

15 - 30 Lacs

Posted:4 days ago| Platform: Naukri logo

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Job Type

Full Time

Job Description

TCS is Hiring!!!

Greetings from TCS!!!

Data Engineer

Hadoop Developer

Desired Experience Range: 5-10 Years

Location: Pune/Bangalore

Desired Competencies (Technical/Behavioral Competency)

Must Have

  • Strong experience in programming languages such as Python, Java, or Scala for data manipulation and engineering tasks.
  • Expertise in SQL and NoSQL databases
  • Hands-on experience with big data technologies like Hadoop, Spark, Kafka, and Hive to handle large-scale data processing and real-time data streams.
  • In-depth knowledge of data warehousing solutions such as Amazon Redshift, Google BigQuery, and Snowflake for building and managing data warehouses.
  • Proficiency in designing, developing, and maintaining ETL (Extract, Transform, Load) processes using tools like Apache NiFi, Talend, or Informatica.

Good to have-

  • Familiarity with cloud platforms like AWS, Azure, or Google Cloud for deploying, managing, and scaling data infrastructure and services.
  • Strong understanding of data modeling concepts and techniques to create efficient and scalable data models.
  • Experience with version control systems such as Git for code management and collaboration.
  • Knowledge of data governance, data quality standards, and data security practices to ensure compliance and protection of sensitive information

Roles and Responsibilities-

  • Design, develop, and maintain scalable data pipelines for extracting, transforming, and loading data from various sources to ensure seamless data flow and accessibility.
  • Collaborate with cross-functional teams to integrate data from multiple disparate sources, ensuring consistency, accuracy, and reliability of data.
  • Optimize data processing workflows and storage solutions for performance, scalability, and cost-efficiency
  • Implement data quality checks and validation processes to ensure the accuracy, completeness, and consistency of data throughout the data lifecycle.
  • Monitor the performance of data pipelines and infrastructure, identifying and resolving issues to maintain system stability and reliability.

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