Lead Data Analyst

Description

Company Overview

Since 2007, BN (an Augeo company since our acquisition in 2023) has been a leader in social media tech innovation and media excellence. BN's mission has been to provide cutting-edge social advertising solutions that allow brands to maximize their brand voice on social media and drive meaningful results to their bottom line with our suite of tools:

• BN Influencer, which enables brands to transform their employees and fans into influencers to create authentic social content on the brand's behalf, all while abiding by brand safety standards.
• BN Ads, our in-house media agency that has worked with Fortune 500 clients for over 10 years.
• BN Innovate, our tech innovation hub, which partners with social platforms and brands including Amex, Meta, TikTok, X, Snapchat, and Pinterest to create technologies that sit on top of their ad buying platforms and solve business objectives with our tech expertise.

BN has offices in Rochester, NYC, Boston, Bentonville, Kansas City, and Hyderabad in addition to Augeo's offices across the country.

Description

As a Lead Data Analyst on the Data Science Team, you'll be the “analytics quarterback” on My Local Social (MLS), our store-level social marketing program spanning Walmart US, Walmart Canada, Sam's Club US, and Sam's Club Mexico. You won't just own MLS reporting and analytics; you'll be the person other teams look to when a measurement, data quality, or reporting-impact question touches MLS, and the one who sets the standards and definitions of success the rest of the program works from. You will also be the primary point of contact for day-to-day external MLS stakeholders. This role reports to the Director of BI (lead of the Data Science team).

The role has two halves. The first is ownership: the recurring client and internal reporting, the data quality standards behind it, and the database and ETL processes that feed all of it, as well as setting the standards other analysts and stakeholders follow when working with MLS data. The second is influence at a program level: establishing what success looks like for new features and pilots, making sure the right behavior is actually being tracked, validating launches, and turning what you find into recommendations the team acts on. We expect the balance to shift toward the second over time as you build trust and the reporting layer becomes more automated.

This position is expected to spend a majority of its time on program-wide measurement, coordination, and escalation rather than hands-on report building. That said, you should be ready to step into direct execution yourself when it's the highest-complexity or highest-risk item on the table.

The right person brings strong SQL and BI depth, real product intuition, and the judgment to notice when a technically correct number is still telling the wrong story. You should be comfortable deciding which questions are worth asking rather than waiting for them to be assigned.

• Serve as the primary analytics point of contact and escalation path for MLS across all four tenants, coordinating between Client Services, Engineering, and other analysts so that measurement decisions, definitions, and priorities are made once and applied consistently.
• Own end-to-end delivery of recurring and ad hoc MLS reporting across all four tenants for client and internal stakeholders, ensuring accuracy, timeliness, and consistency across all outputs, while setting the standards and definitions that govern how other contributors report on MLS. Delegate routine report builds to other analysts and personally handle only escalations, complex builds, or client-sensitive items.
• Set the technical standard for the Power BI reports and Metabase dashboards for MLS, along with the PostgreSQL staging tables, views, and SQL that feed them. Day-to-day build and maintenance should be delegated to other analysts wherever possible; step in directly only for the highest-complexity or highest-risk items.
• Own data quality checks and validation as the last line of defense before data reaches the client, including frameworks that catch anomalies, missing values, and inconsistencies at the source, and coach other analysts to catch issues earlier in the pipeline rather than reviewing everything yourself.
• Lead definition of success criteria and measurement plans for new features and pilots, from discovery through launch and into optimization, owning the decision when stakeholders disagree on approach.
• Represent MLS analytics in cross-functional planning, flagging measurement or data implications early enough that they shape decisions rather than simply react to them.
• Analyze adoption, engagement, and funnel drop-off to show how the product is actually being used and wher