Sr Analyst, Data Analytics

Madison Reed•San Francisco, CA

1d•$130,000 - $140,000•Remote

About The Position

The Senior Analyst, Data & Analytics is a core member of Madison Reed’s centralized Data & Analytics organization. This role turns operating and customer data into clear, decision-ready recommendations for the business. You will work directly with DTC, Marketing, Product, Supply Chain, Finance, and analytics teammates. Your initial focus will be customer behavior, retention, and product measurement across Madison Reed’s customer journeys and channels, plus the analytics that support Supply Chain decisions. You will own analytical work from the first question through the recommendation.

Requirements

Strong analytical judgment and curiosity. You look for the driver behind a number and can separate a real signal from noise.
Advanced SQL skills and experience using Python for analysis, and a disciplined approach to query design, validation, and documentation.
Clear written and verbal communication. You can explain a recommendation and its caveats in plain language.
Ownership and initiative. You can work independently, ask the right questions, and move an analysis forward without waiting for every detail.
Strong organization and prioritization in a fast-moving environment with competing requests.
Comfort working with a range of stakeholders, from operating teams to senior leaders.
Degree in a quantitative field or equivalent experience in statistics, applied mathematics, analytics, operations research, econometrics, computer science, or applied science.
4+ years of experience in data analytics, business intelligence, or decision support.
Advanced SQL with the ability to design and validate complex queries.
Experience creating dashboards, defining business metrics, and communicating analysis to stakeholders.
Experience with product analytics, experimentation, retention, or lifecycle measurement

Nice To Haves

Python experience for analysis, automation, or data preparation is preferred.
Experience with modern analytics tooling, ideally Snowflake, dbt, Looker, and Hex.
Experience in ecommerce, consumer, retail, subscription, or service operations is preferred.
Consulting or customer insights experience is a plus.
Familiarity with marketing measurement, incrementality, MTA, or MMM is a plus.

Responsibilities

Own analyses of customer behavior, retention, conversion, and funnel performance across channels and customer journeys. Identify the drivers behind performance and recommend actions partners can take.
Partner with Product on behavioral measurement and with Marketing on lifecycle decisions. Support Supply Chain and Finance partners with decision support as priorities evolve.
Translate ambiguous business questions into a clear analytical approach, validate the results, and present a recommendation with the relevant caveats.
Write SQL to explore, validate, and analyze data in Snowflake. Create reliable dashboards, reports, and reusable analytical tools in Looker and Hex using the team’s governed models and metrics.
Partner on measurement plans and test readouts with Product and business teams. Help define the metrics, checks, and interpretation that make results trustworthy.
Use AI tools to accelerate data exploration, SQL development, and insight generation, validating their output before use in business decisions.
Investigate data issues, reconcile results across sources, and make data limitations clear before work reaches business partners.
Help turn recurring questions into governed metrics and self-service reporting. Work with the Senior Manager and analytics engineering on changes to dbt models and LookML when needed.
Document analytical logic and partner with analytics engineering on reusable models and semantic-layer improvements.
Build AI-ready datasets with clear grain, documented business definitions, and quality checks, partnering with analytics engineering on appropriate access controls.
Build direct relationships with DTC, Marketing, Product, Supply Chain, and Finance partners. Understand the decision behind each request, share progress early, and keep the work focused on the decision rather than the report.
Communicate results clearly to both technical and non-technical audiences. Explain what the data supports, what it does not, and what should happen next.