Sr. Data Analyst

Day-to-Day Responsibilities

Data Ownership & ETL:-

• Take end-to-end ownership of high-frequency datasets (e.g., seller discoverability, catalogue quality, order funnels, SLA/TAT adherence, compliance).
• Build and maintain automated ETL pipelines using SQL, Python, and Power Automate.
• Ensure datasets are accurate, reliable, and regularly refreshed.
• Validate and optimize datasets to make them trusted single sources of truth.

Dashboarding & Reporting:-

• Develop and manage Power BI dashboards with role-level security.
• Provide real-time visibility into order journeys, operational KPIs, and category growth.
• Improve dashboard usability, performance, and accuracy.
• Automate insight emails/reports highlighting trends, anomalies, and actions for leadership.

Operational, API & Growth Analytics:-

• Monitor key operational metrics — TAT breach, fill rate, cancellations, delivery aging, SLA adherence.
• Analyze API logs and event-driven datasets to understand order lifecycle behavior.
• Ensure log compliance across buyer and seller platforms.
• Build frameworks like Network Participant (NP) Scorecards to track performance.
• Partner with category teams to identify order drop-offs, visibility gaps, and growth opportunities.

Stakeholder Engagement:-

• Collaborate across internal teams to solve operational and strategic challenges.
• Work closely with Buyer Apps, Seller Apps, and Logistics Partners to improve catalogue onboarding, order flow, and fulfillment.
• Present insights and recommendations to senior leadership in a business-focused manner.

Ideal Candidate Profile

Education & Experience:-

• Master’s degree in Computer Science, Statistics, Data Science, Economics, or related field.
• 5+ years in data processing and pipeline building.
• Experience with DAX queries or BI pipeline creation.
• Preferably 2+ years in e-commerce, retail, or logistics analytics.
• Strong understanding of digital commerce funnel — catalog → search → cart → order → fulfillment → resolution.

Technical Skills:-

• Advanced SQL/PostgreSQL for querying, modeling, and ETL.
• Strong Power BI skills (dashboarding, optimization, security).
• Python for data automation and analysis.
• Excel (advanced) for ad-hoc analysis.
• Experience with API or transactional datasets (order lifecycle events, SLAs, logs).

Behavioral & Soft Skills:-

• Business-first mindset and analytical problem-solving.
• Ownership mindset for data accuracy and reliability.
• Strong communication — turning data into actionable insights.
• Collaborative in fast-paced, cross-functional environments.