ETL Developer

Required Skills

Strong ETL ,SQL expert with knowledge on python.

• ETL concepts
• Data Warehousing concepts
• Advanced SQL Concepts
• Data Validation/Data Quality Check
• CI/CD techniques.
• Programming language: JAVA, Python
• Cloud Platform: GCP

• Manage the end-to-end lifecycle of data pipelines, including extraction, transformation, and loading into the Google Warehouse (GCP).
• Conduct in-depth data analysis and apply statistical methods to derive insights.
• Execute code development within the designated DEV environment.
• Perform comprehensive validation and testing prior to deploying code to UAT or other staging environments.
• Document and maintain records of all test outcomes.
• Facilitate the code submission process by preparing Change Lists for peer review.
• Oversee the final deployment of code across multiple environments, including UAT, PREPROD, and PROD.

• Develop and manage ETL data pipelines to populate the data warehouse using various custom and third-party systems.
• Create, deploy, and refine comprehensive full-stack Data and BI solutions, covering everything from extraction and storage to transformation and visualization.
• Utilize SQL and Python to build and maintain robust data analysis scripts.
• Provide ongoing support and development for dashboards and reports via Google PLx and Looker Studio.
• Enhance existing business intelligence tools and create new dashboards to drive organizational growth.
• Conduct detailed data examinations and apply statistical analysis techniques.
• Monitor performance and implement necessary infrastructure optimizations
• Demonstrate excellent collaboration, interpersonal communication and written skills with ability to work in a team environment.

• Candidates must possess at least 6-8 years of professional experience.
• Due to the high-velocity nature of this project, individuals with extensive experience will achieve the most effective results.

• Design, develop, and maintain scalable and robust ETL/ELT processes and data pipelines using various tools and technologies.
• Build and optimize data warehouses, data lakes, and other data storage solutions to support analytical and operational needs.
• Implement data quality checks and monitoring to ensure the accuracy, completeness, and consistency of data.
• Work with large datasets, performing data modeling, schema design, and performance tuning.
• Create data models that are easy for BI tools to consume and build dashboard.