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Source: US DOL Office of Foreign Labor Certification, LCA disclosure data, FY2026 Q2. Government data is published roughly one quarter in arrears.
This advert was matched to Senior Engineer II, a job title CoreWeave, Inc. has itself sponsored.
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The role, as CoreWeave, Inc. describes it
CoreWeave is The Essential Cloud for AI™. Built for pioneers by pioneers, CoreWeave delivers a platform of technology, tools, and teams that enables innovators to build and scale AI with confidence. Trusted by leading AI labs, startups, and global enterprises, CoreWeave combines superior infrastructure performance with deep technical expertise to accelerate breakthroughs and turn compute into capability. Founded in 2017, CoreWeave became a publicly traded company (Nasdaq: CRWV) in March 2025. Learn more at www.coreweave.com .
What You'll Do:
We're hiring a Senior Analytics Engineer to own analytics for the Machine Infrastructure Supply Chain team at CoreWeave. In this role, you'll build and evolve our enterprise semantic layer - transforming raw data into trusted models, metrics, and analytics-ready datasets used by leaders and teams across the company. You'll work across the full analytics stack, using tools like dbt, Airflow, Spark, StarRocks, Tableau, and Omni to deliver everything from executive dashboards and AI analyst agents to well-defined, reusable data models. You'll partner closely with data engineering, finance, product, and operations to define shared business logic and ensure data is clear, consistent, and decision-ready. This is a high-ownership role for someone who wants to shape how the company understands its data and how decisions get made.
About the role:
- Analyze inventory trends and generate reports to drive decision-making and support supply chain optimization.
- Monitor and maintain optimal inventory levels across the supply chain network, balancing service levels with working capital targets.
- Collaborate with demand planners and buyers to align inventory strategies with sales forecasts and promotional plans.
- Lead root cause analysis for inventory discrepancies, stockouts, overstock, and slow-moving items.
- Identify and implement process improvements related to inventory planning, replenishment, and warehouse efficiency.
- Develop KPIs and dashboards to track inventory accuracy, turns, cycle times, and other performance metrics.
- Support inventory reconciliation and cycle count programs in coordination with warehouse and finance teams.
- Assist with ERP system optimization, particularly related to inventory modules, safety stock parameters, and reorder points.
- Provide insights into inventory risk and obsolescence to support product lifecycle management.
Who You Are:
- 5+ years of experience in Analytics Engineering, Data Engineering, or Business Intelligence, with ownership of production analytics systems.
- 5+ years of hands-on experience modeling analytics-ready data using dbt with SQL and/or Python.
- Expert-level SQL, including writing, optimizing, and debugging complex analytical queries.
- Deep experience querying MPP analytical databases such as StarRocks, Snowflake, BigQuery, or Redshift.
- Strong experience with one or more modern BI tools (e.g., Omni, Tableau, Looker, Power BI), including building semantic models, reusable metrics, and executive dashboards to enable consistent, self-serve analytics.
- Hands-on experience orchestrating data pipelines with Airflow, Dagster, or equivalent.
- Working proficiency in at least one scripting language (e.g., Python, Bash, R, or Julia).
- Proven ability to translate complex data into trusted models, metrics, and visualizations used by senior stakeholders.
Preferred:
- Experience building and operating production data pipelines or data platforms, with strong software engineering practices (testing, CI/CD, code review).
- Deep hands-on experience with stream processing and transport systems (e.g., Flink, Kafka), including performance tuning and failure debugging.
- Familiarity with open table formats such as Iceberg, Hudi, Delta, or Paimon, including an understanding of how they impact data modeling, reliability, and analytics performance.
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