Associate Data Engineer

Associate Software Engineer

Job Description
Education: B.E. in Computer Science or Information Technology
Experience: 1-3+ years
Position Requirements
Advanced proficiency in SQL across traditional relational databases (e.g., PostgreSQL) and columnar data warehouses (e.g., Vertica), GBQ, DorisDB
Strong aptitude for AI, with deep understanding of ML and LLMs
Ability to troubleshoot data integrity issues independently
Experience with dashboard development using platforms such as Tableau or Looker
Experience working closely with non-technical stakeholders, including Business Users, Business Intelligence, and Marketing teams
Solid understanding of Web Analytics, business metrics, KPIs, and reporting frameworks
Understanding of multi-channel data, such as Digital Ads stack and Email marketing data, is preferred

Experience with the development of ETL / ELT data pipelines using tools such as Informatica, Pentaho, or similar tools
Excellent data analysis, communication, and technical documentation skills
Note: The core underlying requirement for this position is adaptability and a strong willingness to learn any of the mentioned tools, as well as AI tools and methodologies to improve SDLC processes and support new platform development. Training on specific internal tools will be provided. Please note that this is a Data Development/Engineering role, not a QA position.

Roles & Responsibilities
Contribute to the development of an enterprise-level Data Quality Platform to ensure end-to-end data quality
Onboard data quality scenarios into the automated Data Quality Platform for continuous monitoring and issue reporting
Evaluate data pipelines across the Data Services team to ensure optimal performance, proper design, and adherence to business requirements
Perform statistical analysis on large datasets in a performant and cost-effective manner to determine data quality and integrity
Develop dashboards to track and display Data Quality statistics
Collaborate with database developers to optimize data processes and identify workflow gaps
Run data queries to identify coding issues and enforce data expectations
Gather data from all project data sources to identify and interpret trends
Report data quality issues and findings to management to inform business decisions and maintain pristine datasets
Master in-house Data Quality & Analytics Platforms (DQAP/DQAI) and leverage AI tools for monitoring, analysis, and reporting
Document data lineages and processes as Visio or Miro diagrams, while maintaining changes to data records