Data Engineer

Netlink Computer Inc; Maharashtra; On-site; INR 1,800,000 - 2,800,000; Full time

Job summary

Netlink Computer Inc. is hiring a Data Engineer to design, build, and maintain scalable data pipelines using Python, PySpark, and Databricks on AWS. The role collaborates with data scientists, analysts, and engineers to ensure high-quality data processing across large datasets.

The position requires 5+ years of experience and is based in Pune, Gurgaon, or Noida in a full-time capacity. You will develop ETL workflows, implement ingestion logic, and maintain APIs for data access.

Qualifications

• Design, develop, and maintain scalable data pipelines using Python, PySpark, and Databricks on AWS.
• Collaborate with data scientists, analysts, and engineers to ensure smooth data processing and quality.
• Build and optimize complex ETL workflows for large datasets.

Responsibilities

• Design, develop, and maintain efficient data pipelines using Python, PySpark, and Databricks on AWS.
• Collaborate with data scientists, analysts, and engineers to ensure data integration and high data quality.
• Build and optimize ETL workflows to handle large datasets.
• Implement data ingestion and transformation logic with PySpark on Databricks to boost performance.
• Write SQL queries to extract and transform data from relational databases.
• Develop, deploy, and maintain APIs for data access and integration.
• Monitor and troubleshoot pipelines for reliable operation.

Skills

Python

PySpark

Databricks

AWS

SQL

ETL

APIs

Data pipelines

Data quality

Tools

Databricks

S3

Redshift

RDS

EMR

Job description

Job Category: Data Services

Job Type: Full Time

Job Location: Pune Gurgaon noida

Experience: 5+ Years

• Design, develop, and maintain efficient and scalable data pipelines using Python, PySpark, and Databricks on AWS.
• Work closely with data scientists, analysts, and other engineers to ensure smooth data integration and high-quality data processing.
• Build and optimize complex ETL (Extract, Transform, Load) workflows to handle large datasets.
• Implement data ingestion and transformation logic using PySpark on Databricks to improve data processing performance.
• Ensure data quality, accuracy, and consistency across all data pipelines.
• Collaborate with cross-functional teams to identify and resolve bottlenecks in data systems.
• Utilize AWS cloud services (e.g., S3, Redshift, RDS, EMR) for data storage and management.
• Write SQL queries to extract, manipulate, and transform data stored in relational databases.
• Develop, deploy, and maintain APIs for integrating data systems and enabling data access.
• Monitor and troubleshoot data pipelines, ensuring they operate smoothly with minimal downtime.
• Stay up to date with the latest industry tools, technologies, and best practices in cloud data engineering and big data processing.

• Design, develop, and maintain efficient and scalable data pipelines using Python, PySpark, and Databricks on AWS.
• Work closely with data scientists, analysts, and other engineers to ensure smooth data integration and high-quality data processing.
• Build and optimize complex ETL (Extract, Transform, Load) workflows to handle large datasets.
• Implement data ingestion and transformation logic using PySpark on Databricks to improve data processing performance.
• Ensure data quality, accuracy, and consistency across all data pipelines.
• Collaborate with cross-functional teams to identify and resolve bottlenecks in data systems.
• Utilize AWS cloud services (e.g., S3, Redshift, RDS, EMR) for data storage and management.
• Write SQL queries to extract, manipulate, and transform data stored in relational databases.
• Develop, deploy, and maintain APIs for integrating data systems and enabling data access.
• Monitor and troubleshoot data pipelines, ensuring they operate smoothly with minimal downtime.
• Stay up to date with the latest industry tools, technologies, and best practices in cloud data engineering and big data processing.

• Design, develop, and maintain efficient and scalable data pipelines using Python, PySpark, and Databricks on AWS.
• Work closely with data scientists, analysts, and other engineers to ensure smooth data integration and high-quality data processing.
• Build and optimize complex ETL (Extract, Transform, Load) workflows to handle large datasets.
• Implement data ingestion and transformation logic using PySpark on Databricks to improve data processing performance.
• Ensure data quality, accuracy, and consistency across all data pipelines.
• Collaborate with cross-functional teams to identify and resolve bottlenecks in data systems.
• Utilize AWS cloud services (e.g., S3, Redshift, RDS, EMR) for data storage and management.
• Write SQL queries to extract, manipulate, and transform data stored in relational databases.
• Develop, deploy, and maintain APIs for integrating data systems and enabling data access.
• Monitor and troubleshoot data pipelines, ensuring they operate smoothly with minimal downtime.
• Stay up to date with the latest industry tools, technologies, and best practices in cloud data engineering and big data processing.