Data Engineer

Overview

Key Responsibilities:

• Develop, maintain, and optimize ETL pipelines using Python and Apache Airflow.
• Work extensively with SQL, GCP BigQuery (BQ), Spark, Hive, and HDFS to create scalable data solutions.
• Design and implement data models for both structured and unstructured datasets.
• Manage and process large-scale datasets efficiently for HR data migration/conversion projects.
• Collaborate with cross-functional teams to ensure data integrity, security, and compliance.
• Troubleshoot data pipeline issues and optimize system performance.

Required Skills & Experience:

• 5+ years of hands-on experience as a Data Engineer.
• Proficiency in SQL, GCP BigQuery (BQ), and Python for data processing.
• Strong expertise in ETL pipeline development using Apache Airflow.
• Experience with big data technologies like Spark, Hive, and HDFS.
• Familiarity with HR data migration or conversion projects is a plus.
• Ability to thrive in fast-paced environments while delivering scalable solutions.

Job Type: Full-time

Pay: ₹70,000.00 - ₹80,000.00 per month

Benefits:

• Provident Fund

Schedule:

• Monday to Friday

Supplemental Pay:

• Performance bonus

Education:

• Bachelor's (Required)

Experience:

• Python,SQL, Google Cloud Platform (GCP), and BigQuery (BQ): 5 years (Required)
• ETL pipeline development (Python,Apache Airflow): 5 years (Required)
• Big data technologies like Spark, Hive, and HDFS: 5 years (Required)
• Data modeling, large-scale datasets: 5 years (Required)
• data integrity, security: 5 years (Required)
• Cloud-based Data Solutions: 5 years (Required)
• CI/CD & DevOps: 5 years (Required)
• Problem-Solving & Analytical Thinking: 5 years (Required)
• HR Data Migration/Conversion: 5 years (Required)
• Performance Optimization: 5 years (Required)

Language:

• English (Required)

Location:

• Remote (Required)

Work Location: Remote