Senior Data Analyst, Labor Operations

About Stord Stord is The Consumer Experience Company powering seamless checkout through delivery for today's leading brands. We are rapidly growing and on track to double our revenue in the next 18 months, fueling operational excellence across a multi‑brand, multi‑site network. We manage over $10 billion in commerce annually through fulfillment, warehousing, transportation, and an operator‑built software suite that includes OMS, Pre‑ and Post‑Purchase, and WMS platforms. Our goal is to level the playing field for all brands, helping them increase cart conversion, improve margins, and drive customer loyalty. The Role This position sits in Stord’s Data team and owns the analytics product layer for our Labor Management System. Working alongside product and engineering, you’ll translate operational needs into data products, write SQL, build dashboards, set metric definitions, and ensure data reliability for day‑to‑day decisions. What You’ll Own End‑to‑end analytics layer for the Labor Management System: requirements, build, maintenance, and quality. Primary interface between the Data team and the Operations org, turning operational insights into data product decisions. Reliability of LMS data feeds, handling data‑quality issues and coordinating with engineering. Partnership with the LMS product manager and engineering to influence feature scope and data observability. Technical credibility to troubleshoot pipeline versus source‑system issues. Collaboration with data engineering for upstream pipeline accuracy and timeliness. Operator‑Facing Dashboards (In‑Shift, Live) Floor TV dashboards: real‑time OPH, order pace vs. plan, labor utilization, exception flags. Shift‑level summary views for supervisors and building GMs. Timely refresh cadence suitable for in‑shift decision‑making. Analytics Layer for Operations Leadership Reporting layer for weekly performance analysis—OPH summaries, site comparisons, trend views. Decomposition framework distinguishing productivity gains from brand‑mix, volume, and complexity effects. Reliable tooling that allows the Operations analytics team to interpret results independently. Methodology and Metric Ownership Define and calculate OPH, UPH, UPO, labor utilization, and related KPIs. Maintain frameworks attributing OPH changes to their root causes. Document definitions and methodology for team clarity. Data Quality and Integrity First line of defense on LMS data issues: migrations, source reconciliation, anomaly detection. Flag, document, and recommend handling for data irregularities (e.g., hours charged with no shipments). Partner with engineering to ensure LMS and WMS data flows are reliable and well understood. What We’re Looking For Must‑Haves Track record as the interface between data and an operational business unit, trusted to solve problems independently. Experience working with fulfillment center or 3PL operations (3‑6 years). Strong SQL skill—querying raw operational data directly. Proficiency in a visualization tool (Tableau, Power BI, or equivalent). Methodological depth—experienced with decomposition, attribution, or waterfall analyses. Operational fluency around OPH/UPH/UPO and labor utilization. Bias toward rapid delivery and iterative prototyping. AI‑first mindset: using AI to write code, analyze, and present results. You must be able to sit with a building GM, understand their decisions, and return a dashboard spec without being walked through requirements. Strong Preference Background at a major 3PL or large‑format retailer. Python (pandas, numpy, data wrangling) for analysis. Familiarity with Labor Management Systems such as Manhattan, Infor, Kronos, or Blue Yonder. Analytics engineering experience (dbt, lightweight transforms, reusable data models). Experience comparing building‑level performance across a multi‑site network. The Environment The Operations org is your customer; your success is measured by their ability to work with your data products. You’ll be the primary relationship between the Data team and the Operations org, with full product ownership. Small Data team with broad scope—contribute from day one and gain visibility up to senior leadership. Tech stack: GCP Datastreams / BigQuery, DBT, GitHub, AI tooling, BI tooling, Fivetran, and more. #J-18808-Ljbffr hackajob