Responsibilities:

Data Ownership & ETL

- Take end-to-end ownership of high-frequency datasets including seller discoverability, catalogue quality, order funnels, SLA/TAT adherence, and compliance datasets.

- Build and maintain automated ETL pipelines using SQL, Python, and Power Automate, ensuring reliability, accuracy, and timely refresh.

- Proactively validate and optimize datasets so that stakeholders can trust them as single sources of truth.

Dashboarding & Reporting

- Develop and manage Power BI dashboards with role-level security that provide real-time visibility into order journeys, operational performance, and category-level growth.

- Continuously improve dashboard usability, performance, and accuracy.

- Deliver automated insights mails and reports summarizing trends, anomalies, and action points for leadership and network participants.

Operational, API & Growth Analytics

- Monitor and analyze key operational metrics: TAT breach, fill rate, cancellations, delivery aging, and SLA adherence.

- Work with API-based logs and event-driven datasets to understand order lifecycle behavior, identify drop-offs, and ensure log compliance across buyer and seller platforms.

- Build data frameworks (e.g., NP Scorecards) to evaluate participant performance across order fulfillment, SLA compliance, and customer issue resolution.

- Partner with category pods to identify order journey drop-offs, catalogue visibility issues, and growth opportunities in supply-demand alignment.

Stakeholder Engagement

- Collaborate with internal teams to solve operational and strategic challenges through data.

- Work closely with Buyer Apps, Seller Apps, and Logistics Partners to identify and address data-driven challenges in catalogue onboarding, product discovery, order flow, and fulfillment.

- Present insights and recommendations to senior leadership and network participants in a clear and business-focused manner.

Requirements

1. Education & Experience

- Master's degree in Computer Science, Statistics, Data Science, Economics, or a related quantitative field.

- 5+ years of experience in data processing and building data pipelines based on business logic.

- Should have worked on DAX queries or creating data pipelines for a BI tool.

- Preferably atleast 2 years in e-commerce, retail, or logistics analytics. And strong understanding of the digital commerce funnel: catalog ingestion, search & discovery, cart & checkout, order confirmation, fulfillment SLAs, and post-purchase resolution.

2. Technical Skills

- Proficiency in SQL ,PostgreSQL for advanced querying, data modeling, and ETL.

- Strong experience with Power BI (dashboarding, role-level security, optimization).

- Hands-on experience in Python for data processing, automation, and statistical analysis.

- Advanced user of Excel for ad-hoc analysis and modeling.

- Familiarity with analyzing transactional or API-driven datasets (e.g., order lifecycle events, SLA tracking, compliance logs).