Analyze learning, assessment, coding, and hiring-platform data to define KPIs, build Power BI dashboards, maintain data pipelines, conduct statistical and cohort analyses, support experimentation, and deliver actionable insights to product, business, customer success, sales, and external clients. The role also mentors junior analysts and collaborates with data science and engineering teams on data quality, feature analysis, and model monitoring.
Job Title: Data Analyst
Location: Coimbatore
Employment Type: Full-time
Experience Level: Senior Associate
About iamneo
Founded in 2016 and now part of the NIIT family, iamneo is a fast-growing, profitable B2B EdTech SaaS company that's transforming how tech talent is upskilled, evaluated, and deployed. Our AI-powered learning and assessment platforms help enterprises and educational institutions build future-ready talent at scale.
Our product suite — including Neo PAT (student employability and placement readiness), Neo Exam (secure, cloud-based examination infrastructure), Neo Colab (coding education management), Neo Coder (AI-powered experiential learning for developers), and Neo Hire (recruitment automation and candidate evaluation) — helps engineering colleges and corporate L&D/HR teams measure, close, and track skill gaps at scale. Every product generates rich behavioral and performance data, and our customers rely on us not just for the platform, but for the insight layer on top of it.
About the Role
We are looking for a Data Analyst to turn the data generated across our learning, assessment, and hiring products into decisions — for our internal product and business teams, and for the insights we surface to universities and enterprise clients. You will partner closely with Product, Engineering, Data Science, Customer Success, and Sales to define metrics, build self-serve and client-facing dashboards, and run analyses that shape roadmap, pricing, and customer outcomes. This is a high-visibility role that sits at the intersection of product analytics and customer-facing insight delivery.
Key Responsibilities
Data Analysis & Reporting
- Own end-to-end analysis of platform data across Neo PAT, Neo Exam, Neo Colab, Neo Coder, and Neo Hire — including assessment performance, coding proficiency, proctoring/integrity signals, learner engagement, and placement/hiring funnel outcomes.
- Define, track, and report on core product and business KPIs — learner engagement, assessment completion and pass rates, skill-gap trends, cohort performance, placement conversion, time-to-hire, and platform adoption/retention.
- Conduct deep-dive analyses on anomalies (e.g., proctoring flags, unusual scoring patterns, drop-offs) and proactively surface risks or opportunities to stakeholders.
Dashboarding & Data Infrastructure
- Design and maintain dashboards and reports in Power BI for internal stakeholders and for client-facing deliverables used by university placement cells and enterprise L&D/HR teams.
- Build and query data pipelines using Google BigQuery, SQL/PL-SQL, and NoSQL sources to support reporting and analysis.
- Set up and maintain ETL pipelines, and work with Data Engineering to define data models and ensure data quality/pipeline reliability.
- Use Python for data manipulation, statistical analysis, and automation of recurring reports.
Collaboration & Stakeholder Support
- Partner with Product Managers to evaluate feature performance, run A/B tests and cohort analyses, and translate findings into actionable roadmap recommendations.
- Support Customer Success and Sales with data-driven narratives — placement outcome reports, ROI analyses, and benchmarking — for university and enterprise renewals and pitches.
- Collaborate with the Data Science/ML team on inputs for adaptive assessment, skill-gap prediction, and recommendation models, including feature analysis and model performance monitoring.
- Mentor junior analysts, establish analytics best practices, and help build a scalable, self-serve data culture across the organization.
Required Qualifications & Skills
- Bachelor's or Master's degree in Statistics, Computer Science, Mathematics, Economics, Engineering, or a related quantitative field.
- 3–6 years of experience as a Data Analyst, ideally in EdTech, SaaS, HR-tech, or a data-rich B2B/B2B2C product environment.
- Strong understanding of SQL, PL/SQL, and NoSQL, with hands-on experience querying large, complex datasets from production and warehouse systems.
- Proficiency in Power BI (certification preferred) and comfort building dashboards for both technical and non-technical audiences.
- Hands-on experience with Google BigQuery for data warehousing and analysis.
- Experience setting up and maintaining ETL pipelines.
- Python scripting for data manipulation, statistical analysis, and automation of recurring reports.
- Solid grounding in statistics — hypothesis testing, experimentation/A-B testing, cohort and funnel analysis.
- Experience translating ambiguous business questions from cross-functional stakeholders into structured analyses and clear, decision-ready recommendations.
- Excellent communication skills, with the ability to present findings to both internal leadership and external clients (university TPOs, enterprise HR/L&D leaders).
Bonus Points
- AWS or GCP experience.
- Prior experience with assessment, LMS, HR-tech, or recruitment-platform data (e.g., test scores, proctoring logs, ATS/hiring funnel data).
- Familiarity with data warehousing and modeling tools (e.g., Snowflake, Redshift, dbt).
- Exposure to product analytics tools (e.g., Mixpanel, Amplitude, GA4) and event-tracking instrumentation.
- Experience working with or supporting Data Science teams on ML feature pipelines or model evaluation.
- Prior experience in a B2B2C SaaS company serving both institutional (university) and enterprise customers.
Why Join Us?
- Be part of a fast-growing, profitable EdTech company backed by the NIIT legacy, working on data from a platform used by leading universities and enterprises (including HCL, LTIM, and others).
- High-impact, high-visibility role with direct exposure to leadership, product strategy, and client-facing outcomes.
- Opportunity to shape the analytics function and data culture as the company scales.
- Collaborative, fast-paced environment at the intersection of AI, education, and workforce transformation.
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