Senior Data Analyst, Labor Operations

Stord is The Consumer Experience Company, powering seamless checkout through delivery for today's leading brands. Stord is rapidly growing and is on track to double our revenue in the next 18 months. To meet and exceed this target, Stord is strategically scaling teams across the entire company, and seeking energetic experts to help us achieve our mission. By combining comprehensive commerce‑enablement technology with high‑volume fulfillment services, Stord provides brands a platform to compete with retail giants. Stord manages over $10 billion of commerce annually through its fulfillment, warehousing, transportation, and operator‑built software suite including OMS, Pre‑ and Post‑Purchase, and WMS platforms. Stord is leveling the playing field for all brands to deliver the best consumer experience at scale. Hundreds of leading DTC and B2B companies like AG1, True Classic, Native, Seed Health, quip, goodr, Sundays for Dogs, and more trust Stord to deliver industry‑leading consumer experiences on every order. Stord is headquartered in Atlanta with facilities across the United States, Canada, and Europe. Stord is backed by top‑tier investors including Kleiner Perkins, Franklin Templeton, Founders Fund, Strike Capital, Baillie Gifford, and Salesforce Ventures. Our fulfillment buildings process tens of thousands of orders daily across an ever expanding network. The data that comes out of those buildings—labor performance, efficiency trends, brand‑level throughput— is central to how we run the business and how we retain and grow our brand relationships. Analytics is a competitive advantage for us, and we’re investing in the people who can unlock it. This role sits in Stord’s Data team and owns the analytics product layer for our Labor Management System. You’ll build alongside the team that’s actively developing the LMS as a product. The Operations org is your customer. Your job is to understand what building GMs and area managers need from their data, and deliver it without needing them to hand‑hold you through requirements. You need to walk into a conversation with a building GM, understand what decisions they’re making and what’s blocking them, and come back with a data product that solves it—not a list of clarifying questions. The operations team should never have to prescribe the solution. If you’ve been the person on a data team who the business actually trusts to understand their problems independently, this is that role. You’ll write the SQL, build the dashboards, set the metric definitions, and sit at the intersection of the LMS product team and the Operations org. The analytical challenge is real—understanding what drives OPH changes across a multi‑brand, multi‑site network requires decomposing volume effects, brand mix shifts, order complexity, and genuine productivity signals. Designing that framework, making it legible to a building GM at 6 a.m., and building it in partnership with the people developing the product underneath— that’s the job. What You’ll Own Define the LMS analytics layer from the ground up, working with a dedicated product team. Build and maintain dashboards without inheriting legacy systems. Ensure the analytics layer meets operational needs and is ready for production use. LMS Data Product Ownership Own the end‑to‑end analytics layer: requirements, build, maintenance, and quality. Act as the primary interface between the Data team and the Operations org for all LMS analytics, translating operational needs into data product decisions. Own the reliability of LMS data feeds into the analytics platform; be the first call when a number looks wrong. Work closely with the LMS product manager and engineering team, joining new feature discussions and setting data observability requirements before they’re built. Diagnose data quality issues, differentiate between analytics pipeline problems and source system problems, and resolve them promptly. Partner with data engineering to ensure upstream pipelines support accuracy and timeliness required for operational dashboards. Operator‑Facing Dashboards (In‑Shift, Live) Floor TV dashboards for area managers: real‑time OPH, order pace vs. plan, labor utilization, and exception flags. Shift‑level summary views for supervisors and building GMs. Timely refresh cadence appropriate for in‑shift decision making. Analytics Layer for Operations Leadership Build and maintain the reporting layer enabling weekly performance analysis—weekly OPH summaries, site comparisons, and trend views. Design and own the decomposition framework that separates genuine productivity gains from brand mix shifts, volume changes, and order complexity effects. Ensure data and tooling are reliable and consistent so the Operations analytics team can interpret results independently. Methodology and Metric Ownership Define and calculate OPH, UPH, UPO, labor utilization, and related KPIs. Design and maintain the analytical framework that attributes OPH changes to root causes. Document definitions and methodology so the broader team understands the numbers. Data Quality and Integrity First line of defense on LMS data issues: system migrations, source reconciliation, and anomaly detection. Flag, document, and recommend handling for data irregularities such as hours charged with no shipments. Partner with data engineering to ensure LMS and WMS data flows are reliable and well understood. Must‑Haves Track record as the interface between a data or analytics team and an operational business unit, trusted to understand problems independently. 3–6 years of operations analytics experience with direct exposure to fulfillment center, 3PL, or warehouse operations. Strong SQL skills; comfortable querying raw operational data from LMS, WMS, or equivalent. Visualization proficiency—Tableau, Power BI, or equivalent; able to build production‑quality dashboards from scratch. Analytical methodology depth—experience designing decomposition analyses, attribution frameworks, or waterfall analyses. Operational fluency with OPH, UPH, UPO, and labor utilization concepts. Bias toward rapid delivery—prototype quickly and iterate rather than seek a perfect solution first. AI‑first mentality—using AI for code, analysis, and result presentation. Strong Preference Background in fulfillment operations analytics at a major 3PL or large‑format retailer. Python for analysis (pandas, numpy, data wrangling). Familiarity with Labor Management Systems: Manhattan Active WM, Infor WFM, Kronos/UKG, Blue Yonder, or similar. Analytics engineering exposure (dbt, lightweight transforms, building reusable data models). Multi‑site fulfillment network context—experience comparing building‑level performance and explaining variance across sites to senior leadership. The Environment Data team member; Operations org is your customer, not your manager. Primary relationship between the Data team and the Operations org for LMS analytics. Full product ownership of the LMS analytics layer, setting metric definitions, and making build decisions. Small Data team with broad scope, working with data engineers and other product‑facing analysts and gaining visibility up to senior leadership. Exposure to all levels of Stord up to and including Senior Leadership. Tech stack: GCP Datastreams/BigQuery, DBT, GitHub, AI tooling, BI tooling, Fivetran, and more. #J-18808-Ljbffr B Capital