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QA Analyst

Job in Navi Mumbai, India
Listing for: WebMD
Full Time position
Listed on 2026-02-04
Job specializations:
  • IT/Tech
    Data Analyst, Data Engineer, Data Warehousing
Job Description & How to Apply Below
About WebMD:
WebMD Health Corp., an Internet Brands Company, is the leading provider of health information services, serving patients, physicians, health care professionals, employers, and health plans through our public and private online portals, mobile platforms, and health-focused publications.
Website :(Use the "Apply for this Job" box below). com /    /

Job Description:

Role:  QA Analyst

Location:

Navi Mumbai( Ghansoli )
Work Timings: 2:00 PM to 11:00 PM

Position Requirement:

• Proficient in SQL (analytical functions, trending, windowing)- Traditional (for e.g. MSSQL, Oracle, Postgre

SQL) or Columnar (like Vertica, Amazon, Redshift) with 3+ years of experience.

• Experience with data QA and ETL / ELT (data pipelines) QA.

• Experience working closely with teams outside of IT (i.e. Business Intelligence, Marketing Adops, Sales).

• Strong understanding of the Web Analytics, metrics, KPIs, and reporting.

• Experience with automating regression tests, reporting platforms (for e.g. Tableau or Pentaho BI) and ETL tools (for e.g. Pentaho or Talend) will be an added advantage.

• Understanding of Ad stack, Email data and data (Ad servers, DSM, DMP etc) is good to have

Role & Responsibilities:

• Performing statistical tests on large datasets to determine data quality and integrity.

• Evaluating system performance and design, as well as its effect on data quality.

• Collaborating with database developers to improve data collection and storage processes.

• Running data queries to identify coding issues and data expectations, as well as cleaning data.

• Gathering data from primary or secondary data sources to identify and interpret trends.

• Reporting data analysis findings to management to inform business decisions and prioritize information system needs.

• Documenting processes and maintaining data records.

• Adhering to best practices in data analysis and collection.

• Keeping abreast of developments and trends in data quality analysis
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