National Data Quality Assurance Officer

About YRG MERF

Established in 1993, YRGMERF is a leading non-governmental organisation in India dedicated to improving health outcomes through integrated healthcare, inclusive partnerships, and research-driven interventions. Guided by the principles of integrity and sustainability, the organisation addresses both immediate and emerging public health challenges, with a strong focus on prevention, care, support, and treatment—particularly in the area of HIV and other infectious diseases.

In India, the TIFA project aims to strengthen national health security through coordinated action across government systems and community stakeholders. This approach is designed to build a resilient ecosystem capable of detecting, preventing, and responding to emerging infectious disease threats.

As part of this initiative, a decentralised community-based surveillance model will be implemented, aligned with the National Centre for Disease Control (NCDC)’s Integrated Health Information Platform (IHIP) and the Integrated Disease Surveillance Programme (IDSP) framework. The model enables community platforms such as Self-Help Groups (SHGs) and Mahila Arogya Samitis (MAS) to identify and report early warning signals using IHIP tools, thereby strengthening community ownership, improving early detection, and enabling timely public health response.

Role Overview

The National Data Quality Assurance Officer designs and operates the project’s data quality assurance systems, maintains the data tracker, conducts data quality checks, and produces the analytical outputs that tell the Project Lead and the donor whether the project is achieving its intended results. This role is the project’s data conscience – the person who ensures that reported numbers are accurate, that progress claims are backed by evidence, and that problems show up in the data before they become crises in the field.

Key Responsibilities

• Design the project’s data quality assurance framework within the first six weeks, including: a results chain from activities to outputs to outcomes, indicator definitions with precise measurement methodology, data sources, collection frequency, responsible persons, and reporting templates
  
• Develop and maintain the project’s central data tracker (Excel or database-based) covering all key indicators: community mobilisation numbers (sentinels identified, trained, active), training outputs (sessions conducted, participants trained by cadre and gender), IHIP reporting (signals reported, verified, time-to-verification, outcome classification), and system performance (verification rate, documentation quality, feedback loop completion)
  
• Receive data from State Data Quality Assurance Officers and District Coordinators on a fortnightly cycle. Cross-check submitted data against training attendance registers, IHIP system records (where accessible), and State Coordinator field visit observations. Flag discrepancies and resolve them before data enters the master tracker
  
• Produce monthly data summaries for the Project Lead: a two-page brief showing progress against each indicator, trends across districts, areas of concern, and recommended corrective actions. The brief should use simple charts and tables that make patterns visible at a glance
  
• Conduct data quality audits: visit each state at least once during the project (ideally around Month 5–6) to physically verify a sample of reported data. Select a random set of reported training sessions and check attendance registers, participant feedback forms, and follow-up records. Select a sample of reported IHIP signals and trace them through the verification chain. Document findings and share with the Project Lead
  
• Prepare the data sections of quarterly donor reports, ensuring that every number reported to the donor has a documented source and has been quality-checked
  
• Support the baseline data collection exercise (Months 1–2) by designing data collection tools, training Outreach Coordinators on their use, and supervising data entry and cleaning. The baseline establishes the starting point against which all subsequent progress is measured – getting it right is critical
  
• Design and support the mid-term and end-line competency assessment exercises. Develop assessment rubrics, train assessors, and analyse results
  
• Maintain a gender-disaggregated data system. Given that ASHAs and ANMs are almost entirely women, and MOs and DSO staff are predominantly men, gender disaggregation is not just a compliance requirement – it reveals whether training is reaching different cadres equitably and whether there are differential barriers to skill uptake.
  

Requirements

Qualifications & Experience

• Post-graduate degree in statistics, epidemiology, public health, demography, or social sciences with quantitative research methods training
  
• Minimum 4 years of experience in NCDC or State IDSP managing zoonotic diseases with at least 2 years managing programme-level data trackers
  
• Strong skills in MS Excel (pivot tables, charting, data validation). Familiarity with Stata, R, or SPSS is desirable for baseline data analysis
  
• Experience designing data collection tools for field use, including awareness of practical constraints (literacy levels of data collectors, connectivity issues, time limitations)
  
• Experience with IDSP or any government health surveillance data system is a strong advantage
  
• Fluency in English and Hindi. Ability to communicate data findings in plain language to non-technical audiences
  
• Willingness to travel to project districts (estimated 4–6 trips during the project period)