Lead Data Analyst

BN, an Augeo company•City of Rochester, NY

19h•$90,000 - $120,000

About The Position

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.

Requirements

4+ years of experience in data and analytics, including time in a product analytics capacity and time owning client-facing reporting.
Demonstrated experience coordinating across multiple teams or stakeholders on a shared program or initiative, including resolving conflicting priorities or definitions without formal management authority.
Comfortable delegating and directing the day-to-day work of other analysts on shared deliverables, even without formal management authority.
Advanced SQL: comfortable with complex queries, CTEs, and window functions, and able to reason about grain and joins and anticipate how a schema change affects downstream reporting.
Hands-on experience building, maintaining, and troubleshooting dashboards and reports in Power BI or comparable tools.
High attention to detail and a low tolerance for errors in client-facing work.
Strong product intuition and a self-directed approach to ambiguity and prioritization, including the ability to spot where analytics should get involved without being asked.
Experience with experimentation and measurement, including A/B testing, pilot versus control design, incrementality, or attribution.
Solid understanding of data quality principles including validation, anomaly detection, data lineage, and root-cause investigation.
Python for data manipulation and automation of analysis and reporting tasks.
Experience analyzing user behavior, feature adoption, and conversion funnels, and defining the instrumentation and measurement plans behind them.
Strong written and verbal communication in English, including the ability to explain data clearly to non-technical stakeholders, write documentation others can actually use, and turn analysis into a clear point of view.
Experience managing multiple recurring reporting deadlines alongside longer-run analytical work.

Nice To Haves

Hands-on experience with Metabase or a comparable SQL-first BI tool.
Exposure to AI, including practical experience applying LLMs to analytical or reporting work.
Exposure to product analytics tooling such as Amplitude, Mixpanel, Heap, or Pendo.
Exposure to large-format retail, social, or digital media data; content moderation or trust and safety reporting; or multi-market reporting.

Responsibilities

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 where it is not working.
Validate launches and monitor live features, building the checks and alerts that surface regressions, instrumentation gaps, and anomalous behavior early.
Run deep-dive analyses that identify, size, and prioritize opportunities to improve product performance, bringing a clear recommendation rather than only a finding.
Design and interpret measurement approaches including A/B tests, pilot versus control comparisons, and pre/post analysis, and be direct with stakeholders about when a result is inconclusive.
Partner with Engineering ahead of each release on product and schema changes: assess the reporting impact, write the specs and tickets, and update downstream reports before anything breaks.
Document report logic, data sources, refresh schedules, known caveats, and data issue resolution status for all owned reports; contribute to rationalization of the existing report portfolio; and maintain a handover-ready worklog.
Identify opportunities to automate or streamline manual reporting, including AI-assisted workflows that monitor performance and surface insight at scale.
Communicate findings to both technical and executive audiences, including client-facing conversations such as QBRs; quantify the impact of the work you ship; and challenge assumptions when the data does not support them.