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