Senior Data Engineer

Quest Global; Maharashtra; On-site; INR 1,200,000 - 2,400,000; Full time

Job summary

Quest Global is seeking a Data Engineer to support the design, implementation, and maintenance of enterprise ETL processes for a global client base. You will develop scalable code to process data and ensure timely availability, leveraging PySpark and big data frameworks like Spark and Hadoop to optimize pipelines.

You will collaborate with senior engineers, handle data delivery across diverse clients, and apply industry best practices including version control and data validation.

Qualifications

• 4-6 years of experience with modern data platforms.
• Strong hands-on PySpark experience.
• Experience with SQL and big data processing frameworks.
• Familiarity with Cloudera Data Platform (CDP) and Airflow.
• Knowledge of data governance tools such as Apache Ranger.
• Experience with Hive Metastore and distributed data systems.

Responsibilities

• Support the design, implementation, and maintenance of enterprise ETL processes for data platforms, for a global client base.
• Develop scalable and efficient code to process data, ensuring availability and accessibility in a timely manner.
• Leverage big data processing frameworks such as Apache Spark and Hadoop to build and optimize data pipelines.
• Collaborate with senior engineers to address data challenges, contributing to solutions that maintain high data quality.
• Assist in the data delivery process, working alongside Data Engineers and Analysts to support accurate, high-value data solutions across various clients and industries.
• Build strong working relationships with team members and clients, contributing to both local and global projects.
• Learn and apply industry best practices, including version control, code reviews, and data validation, to ensure quality in data processes.
• Use SQL and other database technologies to help optimize data processing and reduce the time required to handle large data sets.
• Design, implement, and maintain data pipelines using ETL frameworks, orchestration tools, and distributed data processing engines.
• Participate in efforts to automate routine data tasks and streamline processes.

Skills

PySpark

SQL

Big data processing

Tools

Cloudera Data Platform

Airflow

Apache Ranger

Hadoop

Iceberg/Parquet

Job description

• Support the design, implementation, and maintenance of enterprise ETL processes for data platforms, for a global client base.


• Develop scalable and efficient code to process data, ensuring availability and accessibility in a timely manner.


• Leverage big data processing frameworks such as Apache Spark and Hadoop to build and optimize data pipelines.


• Collaborate with senior engineers to address data challenges, contributing to solutions that maintain high data quality.


• Assist in the data delivery process, working alongside Data Engineers and Analysts to support accurate, high-value data solutions across various clients and industries.


• Build strong working relationships with team members and clients, contributing to both local and global projects.


• Learn and apply industry best practices, including version control, code reviews, and data validation, to ensure quality in data processes.


• Use SQL and other database technologies to help optimize data processing and reduce the time required to handle large data sets.


• Design, implement, and maintain data pipelines using ETL frameworks, orchestration tools, and distributed data processing engines.


• Participate in efforts to automate routine data tasks and streamline processes.

Job Requirements

• Support the design, implementation, and maintenance of enterprise ETL processes for data platforms, for a global client base.


• Develop scalable and efficient code to process data, ensuring availability and accessibility in a timely manner.


• Leverage big data processing frameworks such as Apache Spark and Hadoop to build and optimize data pipelines.


• Collaborate with senior engineers to address data challenges, contributing to solutions that maintain high data quality.


• Assist in the data delivery process, working alongside Data Engineers and Analysts to support accurate, high-value data solutions across various clients and industries.


• Build strong working relationships with team members and clients, contributing to both local and global projects.


• Learn and apply industry best practices, including version control, code reviews, and data validation, to ensure quality in data processes.


• Use SQL and other database technologies to help optimize data processing and reduce the time required to handle large data sets.


• Design, implement, and maintain data pipelines using ETL frameworks, orchestration tools, and distributed data processing engines.


• Participate in efforts to automate routine data tasks and streamline processes.

Work Experience

Years of Experience: 4-6 Years

Deep understanding and experience with modern Data Platforms – Cloudera Data Platform (CDP)

• Strong hands-on experience with PySpark.


• Experience with Cloudera Data Platform (CDE, CDW, Ozone, Airflow, SDX), Apache Ranger


• Deep understanding of distributed data systems and Hive Metastore


• Experience and understanding of cataloging, lineage, and governance


• Experience / understanding Open Data Contract Standard (ODCS) and its implementation


• Experience working with SQL, file formats (Iceberg/Parquet), and partitioning/bucketing strategies.

Nice to have

• Experience modernizing enterprise finance systems or regulated environments


• Knowledge of CI/CD, data engineering best practices


• Understanding of financial data structures, accounting processes, or reconciliation workflows.