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.
Skills: sql,dax,etl,python

Work arrangement
No