Senior Data Analyst, Labor Operations

Stord•Atlanta, GA

About The Position

Stord is seeking a Senior Data Analyst for Labor Operations to join their Data team. This role will own the analytics product layer for the Labor Management System (LMS), working closely with the team actively developing the LMS as a product. The primary customer for this role is the Operations organization, requiring the analyst to understand and deliver data products that meet the needs of building GMs and area managers without constant guidance. The ideal candidate possesses operational fluency combined with technical execution, able to understand operational challenges and deliver data solutions independently. Responsibilities include writing SQL, building dashboards, defining metrics, and collaborating with the LMS product team and Operations org. The role involves analyzing factors driving operational changes across a multi-brand, multi-site network, including volume, brand mix, order complexity, and productivity signals. Stord is a rapidly growing company focused on consumer experience through seamless delivery, managing significant commerce volume annually with a suite of fulfillment, warehousing, and transportation services. They are backed by prominent investors and operate facilities across the US, Canada, and Europe. Analytics is considered a competitive advantage at Stord, and this role is crucial for unlocking that potential within the Labor Management System.

Requirements

Track record of working as the interface between a data or analytics team and an operational business unit.
Ability to understand operational drivers and translate them into dashboard specifications without extensive guidance.
3-6 years of experience in operations analytics with direct exposure to fulfillment center, 3PL, or warehouse operations.
Fulfillment center or 3PL building experience is key.
Strong SQL skills, comfortable querying raw operational data from an LMS, WMS, or equivalent.
Proficiency in data visualization tools (Tableau, Power BI, or equivalent) with the ability to build production-quality dashboards from scratch.
Depth in analytical methodology, including experience designing decomposition analyses, attribution frameworks, or waterfall analyses.
Operational fluency with concepts like OPH, UPH, UPO, and labor utilization.
Bias toward rapid delivery, prototyping quickly and iterating.
AI First mentality; experience using AI for code, analysis, and summarization.

Nice To Haves

Industrial engineering, operations research, or a quantitative supply chain background, particularly with hands-on analytics work.
Background in fulfillment operations analytics at a major 3PL or a large-format retailer.
Experience with Python for analysis (pandas, numpy, data wrangling).
Familiarity with Labor Management Systems such as Manhattan Active WM, Infor WFM, Kronos/UKG, Blue Yonder, or similar.
Analytics engineering exposure (dbt, lightweight transforms, building reusable data models).
Experience in a multi-site fulfillment network context, including comparing building-level performance and explaining variance across sites to senior leadership.

Responsibilities

Own the end-to-end analytics layer for Stord's Labor Management System: 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.
Work closely with the LMS product manager and engineering team as a core partner, contributing to new feature scoping and data observability requirements.
Address data quality issues, distinguishing between analytics pipeline and source system problems, and collaborating with LMS engineering for resolution.
Build and maintain the reporting layer for the Operations team to conduct weekly performance analysis, including OPH summaries, site comparisons, and trend views.
Design and own the framework to decompose OPH changes into root causes like genuine productivity gains, brand mix shifts, volume changes, and order complexity.
Own the definition and calculation of key performance indicators (KPIs) such as OPH, UPH, UPO, and labor utilization.
Document definitions and methodology for the broader team.
Serve as the first line of defense on LMS data issues, including system migrations, source reconciliation, and anomaly detection.
Flag, document, and recommend handling for data irregularities.
Partner with data engineering to ensure LMS and WMS data flows are reliable and well-understood.
Build floor TV dashboards for area managers displaying real-time OPH, order pace vs. plan, labor utilization, and exception flags.
Create shift-level summary views for supervisors and building GMs.
Ensure timely refresh cadences for in-shift decision-making dashboards.