Hybrid Details: 3 days/week onsite
Duration: 14 months to start 

Job Description:

• Design, implement, and optimize efficient ETL processes to transform raw data into actionable insights.
• Develop and maintain robust data warehouse solutions, including the implementation of star and snowflake schemas.
• Establish and manage reliable data pipelines to ensure timely data availability.
• Create modular, maintainable, and scalable dbt workflows for advanced data transformations.
• Leverage dbt testing, documentation, snapshotting, and Change Data Capture (CDC) for incremental data refresh.
• Implement and manage Type 2 data modelling techniques for historical data.
• Develop reusable macros and packages using Python libraries and dbt packages.
• Optimize complex SQL queries and leverage Snowflake’s performance-enhancing features like Streams, Time Travel, partitioning, and clustering.
• Orchestrate data pipelines for both batch and near real-time data refresh scenarios.
• Write and optimize Snowflake SQL queries and stored procedures for seamless data transformation and integration.
• Ensure compliance with data governance policies and implement security controls for sensitive data.

• Bachelor’s degree in computer science, Information Systems, or a related field.
• 3-5 years of experience in data warehouse development and ETL tools (e.g., dbt, SSIS, Informatica, or Azure Data Factory).
• 1–2 years of experience with dbt and Snowflake.
• Proficiency in SQL/PL-SQL for data querying and optimization.
• Familiarity with Python for enhancing dbt pipelines.
• Strong analytical, problem-solving, and communication skills.

• Hands-on experience with ETL tools such as DBT, SSIS, Informatica, or Azure Data Factory.
• Knowledge of Snowflake, including query writing and data integration.
• Familiarity with cloud platforms like Azure Synapse, AWS Redshift, or Snowflake.
• Experience with Agile methodologies.
• Experience with CI/CD tools like Jenkins, Docker, or Terraform.