Data Analyst, Platform Excellence Ops Analytics

Why this role is on the menu

The Data Analyst position will play a pivotal role on the PEO Analytics team, leveraging operational performance data to diagnose fulfillment quality gaps, spearhead the detection and resolution of quality issues, and deliver data-driven recommendations that improve Instacart's products and processes. This means not only tackling immediate escalations but also establishing proactive monitoring systems that enhance long-term operational excellence, as well as supporting cross-functional insight needs that drive improvements across multiple areas of the business.

The Platform Excellence Operations (PEO) team is dedicated to improving the quality of Instacart's online grocery experience for customers, shoppers, retailers, and the overall marketplace. The analytics team identifies, understands, and eliminates defects in that experience by analyzing and interpreting operational data, ensuring stakeholders have a consistent understanding of why business performance is changing and advocating for improvements that might otherwise be overlooked. 

As part of the Marketplace and Retailer Pillar within PEO Analytics, this role works on fulfillment quality problems ranging from cancellations and reschedules to timeliness, and plays an essential role in maintaining Instacart's reputation for reliability and operational excellence, from metrics analysis to strategy development. You will be part of a dynamic, cross-functional team whose success is built on transparency, communication, and the relentless pursuit of solutions that “Grow the Pie” for all stakeholders involved.

What you'll cook up in your first year

Metrics and Reporting

• Retailer performance issues are proactively monitored and resolved across broad dimensions, with dashboards and processes built to simplify problem diagnosis and support new retailer launches.
• Performance fluctuations and outliers in key Retailer Quality workstream metrics are tracked and analyzed to identify root causes and recommend corrective actions.

Data Analysis

• Large datasets related to Retailer Fulfillment Quality are analyzed to derive business insights, with analytical plans independently created and executed to identify, understand, and reduce fulfillment quality issues with high accuracy and consistency.
• Analytics requests, ad hoc retailer analyses, launch impact assessments, and retailer escalations are supported, with initiative sizing provided for product and analytical roadmaps.

Presentations and Communication

• Insights decks are created and presented regularly to a cross-functional audience of business and technical stakeholders, with findings framed as clear logical narratives backed by data that result in actionable recommendations.

The secret ingredients we're looking for

Must-have pantry staples (Minimum Qualifications)

• 1+ years of experience in a data analytics-related role such as business intelligence, corporate strategy, consulting, or data science.
• Experience analyzing and visualizing large datasets.
• Advanced proficiency in SQL and Excel or Google Sheets for data analysis.
• Ability to aggregate and synthesize large amounts of data, leveraging a deep understanding of micro and macro fulfillment metrics and systems to drive insights.
• Strong problem-solving skills with keen attention to detail; able to quickly detect anomalies in performance metrics and trends, form hypotheses for investigation, and draw conclusions through nuanced analysis.
• Proficiency in PowerPoint or Google Slides for effective communication.
• Ability to work independently with a strong sense of ownership and comfort proposing new ideas to challenge existing processes.

Optional garnishes (Preferred Qualifications)

• Experience working in a consumer marketplace or logistics business.
• Familiarity with Snowflake or similar data warehouses, GitHub, and Jira.
• Experience with visualization tools such as Mode or Tableau.
• Experience working directly with product and engineering teams on scalable process solutions.
• Experience with using AI tools for analytics, including applying methods for data validation and reproducibility

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