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Data Engineer

Job in 600001, Chennai, Tamil Nadu, India
Listing for: Confidential
Full Time position
Listed on 2026-02-04
Job specializations:
  • IT/Tech
    Data Engineer, Big Data
Salary/Wage Range or Industry Benchmark: 760000 - 969999 INR Yearly INR 760000.00 969999.00 YEAR
Job Description & How to Apply Below
Role:  Data Engineer - Databricks

Required Technical Skill Set: AWS/Azure, Databricks, Spark/ PySpark, SQL

Desired Experience Range: 6+ Years

Location of Requirement:
Bangalore / Chennai

Desired Competencies (Technical/Behavioral Competency)

Must-Have  ( Ideally should not be more than 3-5)
Strong experience in programming languages such as Python, PySpark, or Scala for data engineering tasks.
Design, develop, and maintain scalable and robust data pipelines on Databricks
Hands-on experience with cloud technologies, expertise in AWS/Azure data engineering services to handle large-scale data processing and real-time data streams.
In-depth knowledge of data warehousing solutions such as Amazon Redshift, Google Big Query, and Snowflake for building and managing data warehouses.
Proficiency in designing, developing, and maintaining ETL (Extract, Transform, Load) processes using cloud services, ADF, Glue etc

Good-to-Have(Ideally should not be more than 3-5)
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

Responsibility of / Expectations from the Role

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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