Associate Software Engineer

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 solid 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

📌 Associate Data Engineer (Mumbai)
🏢 WebMD
📍 Mumbai