This role sits in Stord's Data team and owns the analytics product layer for our Labor Management System. What makes it genuinely interesting: you're not inheriting a legacy setup - you're building alongside the team 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.
The need this role fills is specific: operational fluency combined with technical execution. 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 trusted 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 6am, and building it in partnership with the people developing the product underneath it - that's the job.
The RoleThis role sits in Stord's Data team and owns the analytics product layer for our Labor Management System. What makes it genuinely interesting: you're not inheriting a legacy setup - you're building alongside the team 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.
The need this role fills is specific: operational fluency combined with technical execution. 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 trusted 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 6am, and building it in partnership with the people developing the product underneath it - that's the job.
What You'll OwnThis is a build role. The LMS analytics layer is being built now, alongside the product and engineering team actively developing the LMS itself. You won't be inheriting a legacy stack or maintaining someone else's dashboards - you'll be defining what good looks like from the ground up, with a dedicated product team to partner with and an Operations org that is ready to use what you build.
LMS Data Product Ownership• 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 - you translate operational needs into data product decisions without the business having to prescribe the solution
• Own the reliability of LMS data feeds into the analytics platform - when a building GM says the numbers look wrong, you are the first call
• Work closely with the LMS product manager and engineering team as a core partner - you'll be in the room when new features are scoped, raising data observability requirements before they're built in, not retrofitting analytics after the fact
• When a data quality issue surfaces, you'll have enough technical credibility to go directly to LMS engineering and distinguish an analytics pipeline problem from a source system problem - and get it resolved
• Partner with data engineering to ensure the upstream data pipeline supports the accuracy and timeliness that operational dashboards require
Operator-Facing Dashboards (In-Shift, Live)• Floor TV dashboards for area managers: real-time OPH, order pace vs. plan, labor utilization, 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 that enables the Operations team to do their own 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 - so the Operations team can answer the "why did OPH change" question themselves
• Ensure the data and tooling is reliable and consistent enough that the Operations analytics team is not blocked or dependent on you to interpret results
Methodology and Metric Ownership
• Own how we define and calculate OPH, UPH, UPO, labor utilization, and related KPIs
• Design and maintain the analytical framework that attributes OPH changes to their root causes
• Document definitions and methodology so the broader team understands what the numbers mean
Data Quality and Integrity• First line of defense on LMS data issues: system migrations, source reconciliation, anomaly detection
• Flag, document, and recommend handling for data irregularities (e.g., hours charged with no shipments)
• Partner with data engineering to ensure LMS and WMS data flows are reliable and well-understood
What We're Looking ForMust-Haves- Track record of working as the interface between a data or analytics team and an operational business unit - you've been the person the business trusts to understand their problems without being walked through requirements.
- Could you sit with a building GM for 30 minutes, understand what's driving their decisions, and come back with a dashboard spec - without your manager or their manager in the room?
- 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
- Industrial engineering, operations research, or a quantitative supply chain background is a strong plus, particularly where it included hands-on analytics work
- Strong SQL - comfortable querying raw operational data from an LMS, WMS, or equivalent without waiting for a pre-built dataset
- Visualization proficiency - Tableau, Power BI, or equivalent; can build a production-quality dashboard from scratch, not just edit existing templates
- Analytical methodology depth - you've designed decomposition analyses, attribution frameworks, or waterfall analyses; you understand the difference between mix effects and rate effects
- Operational fluency - OPH, UPH, UPO, and labor utilization are concepts you've worked with on the floor, not just in a textbook
- Bias toward rapid delivery - you prototype quickly and iterate, rather than seeking a perfect solution before showing your work
- AI First mentality - Stord is an AI first company. Our team uses AI to write code, do analysis, and summarise / present results
Strong Preference- Background in fulfillment operations analytics at a major 3PL or a 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 - you've compared building-level performance and explained variance across sites to senior leadership
The EnvironmentA few things worth knowing before you apply:
- You sit in the Data team. The Operations org is your customer, not your manager. Your success is measured by how well operations leadership and building GMs can do their jobs with the data products you build - not by how many reports you produce.
- You are the primary relationship between the Data team and the Operations org for LMS analytics. Building GMs and area managers will know your name. When a number looks wrong, you're the call.
- Genuine product ownership - you will define what the LMS analytics layer looks like, own the metric definitions, and make calls about what gets built and what doesn't. This is not a role where someone else sets the product direction and you execute tickets.
- Small Data team with broad scope. You'll work alongside data engineers and other product-facing analysts, contribute from your first month, and have visibility into how your work lands operationally.
- You'll get exposure to all levels of Stord up to and including our Senior Leadership team
- Tech stack: GCP Datastreams / BigQuery, DBT, GitHub, AI tooling, BI tooling, Fivetran and more
