ETL Developer

16 days ago

Job Description

Required Skills : Strong ETL ,SQL expert with knowledge on python.

Primary Roles & Responsibilities :

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 ChangeLists for peer review.
Oversee the final deployment of code across multiple environments, including UAT, PREPROD, and PROD.

Job Description:

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.

Minium Qualifaication:

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.

Responsibilities in this role :

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.

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