ETL Developer (Immediate Joiner)

Position: ETL Developer (Snowflake + Python)

Experience: 4–8 years

Location: ( Gurugram / Bangalore )

Joining: Immediate Joiner

Budget: Up to 11 LPA

Job Summary

ETL Developer

Key Responsibilities

• Design, develop, and maintain ETL processes to extract, transform, and load data from various sources into

  Snowflake Data Warehouse

  .
• Write efficient

  Python scripts

  for automation, data transformation, and integration tasks.
• Work with stakeholders to understand data requirements and translate them into technical specifications.
• Optimize ETL workflows for

  scalability, performance, and cost efficiency

  in a cloud environment.
• Manage and monitor data pipelines, ensuring high availability and reliability.
• Implement

  data quality checks

  , validations, and reconciliation processes.
• Collaborate with BI teams, Data Engineers, and Data Analysts to deliver accurate and timely data.
• Maintain documentation for ETL processes, data flows, and architecture.
• Troubleshoot and resolve data-related issues and performance bottlenecks.

Required Skills & Experience

• 4+ years

  of ETL development experience, preferably in a cloud-based environment.
• Strong expertise in

  Snowflake

  (SnowSQL, Snowpipe, Streams, Tasks, Warehouses, etc.).
• Proficiency in

  Python

  for scripting, automation, and data transformation.
• Solid understanding of

  SQL

  (complex queries, optimization, stored procedures).
• Experience with

  data integration tools

  (e.g., Apache Airflow, Informatica, Talend, dbt) is a plus.
• Strong understanding of

  data warehousing concepts

  (star schema, snowflake schema, dimensional modeling).
• Knowledge of

  cloud platforms

  such as AWS, Azure, or GCP.
• Familiarity with version control (Git) and CI/CD for ETL deployments.
• Problem-solving skills with the ability to work independently and in a team.

Preferred Qualifications

• Snowflake

  SnowPro Certification

  or equivalent.
• Experience with

  data lake integration

  and unstructured data handling.
• Exposure to

  machine learning pipelines

  in Python (optional but beneficial).