Data Analyst

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 or any other dashboard tools 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. Ideal Candidate Profile Education & Experience • Bachelor’s degree in Computer Science,Statistics, Data Science, Economics, or a related quantitative field. • 3+ 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. Technical Skills • Proficiency in SQL ,PostgreSQL for advanced querying, data modeling, and ETL. • 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). Behavioral & Soft Skills • Business-first mindset with strong problem-solving orientation. • Ability to own datasets and dashboards end-to-end with accountability for accuracy and reliability. • Strong communication skills to translate data into actionable business insights. • Comfortable collaborating with cross-functional stakeholders in a fast-evolving environment.