Role: Analytics Engineer, Supply Chain Insights (New Role)
Contract length: 12 months
Location: Sunnyvale, CA or Remote
Role Summary
We are looking for an Analytics Engineer to own and evolve the data foundations that drive supply chain visibility and operational excellence. In this role, you will design and build scalable data pipelines, develop analytical models, and deliver trusted datasets and insights that inform critical business decisions. You will also conduct deep statistical analyses, build predictive models, and synthesize findings into actionable strategy — blending analytics engineering with data science to maximize business impact. You will work closely with cross-functional partners in Supply Chain Operations, Finance, Procurement, and Planning to translate complex business requirements into reliable, well-documented data products and actionable insights.
You are expected to independently drive medium-to-large projects end-to-end — from requirements gathering and technical design through implementation, validation, and stakeholder adoption — with minimal guidance.
Responsibilities: Analytics & Insights Delivery
- Design, build, and maintain scalable ETL/ELT pipelines (SQL, Python); Own critical data models spanning demand forecasting, inventory management, logistics operations, supplier performance, and fulfillment metrics
- Build and maintain AI-powered dashboards and reporting tools that provide real-time and historical visibility into supply chain KPIs
- Develop analytical frameworks that quantify supply chain performance — including on-time delivery, lead times, inventory turns, cost-to-serve, and forecast accuracy
- Conduct deep-dive analyses and root cause investigations to diagnose supply chain disruptions, identify bottlenecks, and quantify business impact
- Design and execute statistical analyses (e.g., hypothesis testing, regression, segmentation) to uncover trends and inform strategic supply chain decisions
- Build predictive models and forecasting solutions (e.g., demand sensing, supply risk scoring, lead time prediction) that enable proactive decision-making
- Develop scenario modeling and simulation frameworks to evaluate trade-offs across sourcing strategies, inventory policies, and network configurations
- Synthesize complex analytical findings into clear narratives and executive-ready recommendations, translating data into actionable supply chain strategy
Cross-Functional Partnership
- Collaborate directly with Supply Chain Operations, Sales, and Finance partners to deeply understand business processes and translate requirements into scalable data solutions
- Proactively identify data gaps and opportunities to improve operational visibility, and drive these improvements from concept to production
- Communicate analytical findings and data product capabilities clearly to both technical and non-technical audiences
Technical Excellence & Standards
- Write clean, well-tested, and maintainable code; conduct thorough code reviews and uphold engineering best practices
- Champion data governance and documentation — maintain data dictionaries, lineage documentation, and onboarding guides for key datasets
- Leverage AI tools and workflows fluently to accelerate development, broaden scope, and improve productivity
Minimum Qualifications
- 8+ years of experience in analytics engineering, data engineering, data science, or a related technical role
- Strong proficiency in SQL (Presto/Hive or equivalent) and Python (including pandas, NumPy, or similar data libraries)
- Experience building and maintaining data pipelines and ETL workflows at scale
- Experience with data modeling (dimensional modeling, star schemas, snowflake/databricks) and data warehouse design
- Familiarity with statistical methods (regression, hypothesis testing, time series analysis) and their practical application to business problems
- Demonstrated ability to work independently on medium-to-large projects with minimal guidance
- Strong communication skills with experience partnering across technical and business teams
Preferred Qualifications
- Experience in supply chain, operations, logistics, or manufacturing domains
- Experience building predictive models or forecasting systems (e.g., demand forecasting, anomaly detection, risk scoring)
- Proficiency with machine learning frameworks (scikit-learn, PyTorch, or similar) for applied analytics use cases
- Experience with dashboarding and data visualization (Tableau, or similar)
- Comfort working with AI-assisted development tools to accelerate engineering work
Never miss an opportunity! Create an alert based on the job you applied for.
