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Key Responsibilities and Required Skills for Warehouse Data Analyst

💰 $65,000 - $110,000

Data AnalyticsSupply ChainWarehouse OperationsBusiness Intelligence

🎯 Role Definition

The Warehouse Data Analyst is a data-driven contributor focused on translating operational warehouse, inventory, and fulfillment data into actionable insights that improve throughput, reduce cost-to-serve, and increase accuracy. This role combines advanced SQL and analytics, warehouse management system (WMS) domain knowledge, dashboarding and reporting, and close partnership with operations, supply chain planning, and engineering teams to deliver measurable improvements in productivity, inventory health, and customer service levels.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Inventory Analyst with foundational reporting experience
  • Business Analyst supporting supply chain or operations teams
  • Junior Data Analyst focused on logistics or e-commerce fulfillment data

Advancement To:

  • Senior Warehouse Data Analyst / Lead Data Analyst, Logistics
  • Manager, Supply Chain Analytics or Manager, Warehouse Analytics
  • Director, Supply Chain Data & Insights / Head of Fulfillment Analytics

Lateral Moves:

  • Business Intelligence Analyst (cross-functional)
  • Inventory Planning or Demand Planning Analyst
  • Operations Analyst / Continuous Improvement Specialist

Core Responsibilities

Primary Functions

  • Design, develop, and maintain scalable SQL queries, ETL processes, and data pipelines to consolidate WMS, ERP, TMS, and telemetry data into a centralized analytics warehouse for timely warehouse reporting and analysis.
  • Build and manage operational dashboards and interactive reports (Power BI, Tableau, Looker) that track daily/weekly KPIs such as inventory accuracy, pick/pack rates, putaway efficiency, order cycle time, on-time fulfillment, and dock-to-stock lead times.
  • Perform SKU-level inventory analytics including aging, slow-moving SKU identification, cycle count variance analysis, and recommendations for adjustments to cycle count frequency or inventory control policies.
  • Conduct root cause analysis on inventory discrepancies, returns, chargebacks, and quality issues by triangulating WMS events, scanning logs, and vendor/inbound records and propose remediation plans.
  • Develop demand and replenishment forecasts (statistical and heuristics-based) to optimize safety stock, reorder points, and inter-warehouse transfers to minimize stockouts and excess inventory.
  • Partner with operations managers and continuous improvement teams to define measurable experiments (A/B tests), run pilot programs, and quantify the operational and financial impact of process changes.
  • Create and maintain data models and dimensional schemas optimized for warehouse analytics, including conformed dimensions and fact tables for orders, transactions, labor, and inventory movement.
  • Automate recurring analyses and reporting, reducing manual work and ensuring a single source-of-truth for warehouse performance metrics and executive reporting.
  • Monitor daily ETL jobs and data quality checks; implement anomaly detection and alerting for missing feeds, sudden KPI shifts, and inventory integrity issues.
  • Analyze labor productivity and workforce planning metrics (picks per hour, orders per labor hour, overtime drivers) to support realistic staffing models and variable labor forecasting.
  • Evaluate vendor performance and inbound logistics metrics (lead time variability, ASN accuracy, putaway delays) and provide insights to procurement and supplier management teams.
  • Support capacity planning by modeling throughput vs. resource utilization, peak-season projections, and identifying bottlenecks across receiving, storage, picking, packing, and shipping.
  • Translate stakeholder requirements from operations, supply chain, and finance into clear analytics use cases, acceptance criteria, and prioritized roadmap items for analytics delivery.
  • Maintain strong documentation, data lineage, and technical notes for dashboards, pipelines, and models to promote reproducibility and governance across the analytics organization.
  • Optimize WMS and ERP data usage: map events to business processes, validate transaction semantics, and collaborate with WMS administrators on data capture improvements.
  • Lead cross-functional initiatives to measure and reduce returns rate, chargebacks, and shipping exceptions by deriving insight from historical return reasons and fulfillment event trails.
  • Build predictive models and anomaly classifiers to identify at-risk SKUs, potential stockouts, or process deviations before they become customer-impacting.
  • Provide ad-hoc analytics support to operations leadership during incidents (outages, system migrations, promotions), creating rapid root-cause reports and recommended mitigations.
  • Translate complex technical findings into clear, executive-ready slide decks and operational playbooks that guide day-to-day decision-making on the warehouse floor.
  • Implement and enforce KPI definitions and reporting standards (e.g., correct calculation for fill rate, OTIF, inventory accuracy) across business teams to ensure consistent performance measurement.
  • Collaborate with data engineering and IT to scope and prioritize integrations for new telemetry (IoT scanners, conveyor sensors) and to streamline data capture that improves analytics fidelity.
  • Continuously research best practices in warehouse analytics, fulfillment automation, and supply chain technology to introduce high-impact analytical methods and tools.

Secondary Functions

  • Support ad-hoc data requests and exploratory data analysis.
  • Contribute to the organization's data strategy and roadmap.
  • Collaborate with business units to translate data needs into engineering requirements.
  • Participate in sprint planning and agile ceremonies within the data engineering team.
  • Train warehouse supervisors and operational staff on dashboard interpretation and analytics-driven decision making.
  • Assist with KPI audits and monthly performance reviews for site-level and regional supply chain leaders.
  • Help define and document SLA/OKR metrics for warehouse service levels and present monthly trend analysis to stakeholders.

Required Skills & Competencies

Hard Skills (Technical)

  • Advanced SQL: complex joins, window functions, CTEs, query tuning and performance optimization for large transaction tables.
  • Data warehousing: experience designing dimensional models, star schemas, and managing data in Snowflake, BigQuery, Redshift or equivalent.
  • ETL & orchestration: working knowledge of tools like dbt, Airflow, Fivetran, Stitch, Matillion, or native pipelines to build reliable ingestion and transformation layers.
  • BI & Visualization: hands-on experience building operational dashboards and reports in Power BI, Tableau, Looker, or equivalent, with attention to usability and drill-down flows.
  • Programming for analytics: Python or R for data manipulation, scripting ETL, statistical analysis, and lightweight machine learning models.
  • WMS/ERP domain knowledge: familiarity with warehouse management systems (e.g., Manhattan, Blue Yonder, Oracle WMS, SAP EWM) and ERP integrations.
  • Statistical analysis & forecasting: time-series forecasting, smoothing methods, and basic demand-planning algorithms to support replenishment and safety stock.
  • Data quality & monitoring: building data validation tests, SLA monitoring, and anomaly detection rules to ensure trust in operational metrics.
  • API and integration basics: extracting transactional and telemetry data from SaaS platforms, logs, and third-party vendor systems.
  • Excel & advanced modeling: pivot tables, Power Query, and VBA or similar for rapid prototyping and stakeholder-ready summaries.
  • Cloud data platforms and security: familiarity with AWS, GCP, or Azure analytics stacks and data governance fundamentals (access controls, PII handling).

Soft Skills

  • Clear communicator: able to translate technical analysis into concise, actionable insights for operations and executive audiences.
  • Stakeholder management: experience prioritizing competing requests and negotiating trade-offs across supply chain, finance, and operations teams.
  • Problem-solving mindset: strong root-cause analysis skills and the ability to propose practical, high-impact solutions.
  • Collaboration: works effectively in cross-functional, fast-paced environments with site leads, planners, and engineering teams.
  • Time management & prioritization: balances urgent operational needs with longer-term analytics projects and roadmap items.
  • Attention to detail: meticulous about data accuracy, definitions, and documentation to prevent misinterpretation of KPIs.
  • Change agent: comfortable influencing operational change through data-driven recommendations and piloting process improvements.
  • Continuous learner: stays current on analytics, supply chain trends, and warehouse technologies to drive incremental and transformative improvements.

Education & Experience

Educational Background

Minimum Education:

  • Bachelor's degree in Supply Chain Management, Operations Research, Data Science, Computer Science, Statistics, Industrial Engineering, Business Analytics, or related field.

Preferred Education:

  • Master’s degree in Data Science, Supply Chain Analytics, Industrial Engineering, or MBA with a quantitative focus is a plus.

Relevant Fields of Study:

  • Supply Chain Management
  • Data Science / Analytics
  • Statistics / Applied Mathematics
  • Industrial Engineering
  • Computer Science
  • Business Analytics
  • Operations Research

Experience Requirements

Typical Experience Range: 2–6 years of analytics experience with at least 1–2 years working directly with warehouse, fulfillment, or inventory datasets.

Preferred: 3–5+ years of hands-on warehouse or logistics analytics experience, proven delivery of dashboards and ETL pipelines, demonstrable WMS or ERP integration experience, and prior exposure to forecasting/replenishment models.

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