Value Engineer FinOps & Growth Analytics

Snowflake Computing • Menlo Park, CA

24d

About The Position

Snowflake is about empowering enterprises to achieve their full potential — and people too. With a culture that's all in on impact, innovation, and collaboration, Snowflake is the sweet spot for building big, moving fast, and taking technology — and careers — to the next level. We are looking for a highly analytical and technically strong FinOps Analytics Consultant with a quantitative or data science background . In this role, you will use advanced SQL and Python to analyze large-scale datasets, model cloud consumption behaviors, and create data-driven insights for Snowflake customers. Snowflake platform expertise and FinOps skills are not required — we will train you. What matters most is your ability to work with complex data, derive insights, think in unit economics, and clearly communicate findings to business and technical stakeholders.

Requirements

• 3-5+ years of experience in data science, quantitative analysis, analytics engineering, or applied statistics roles.
• Strong SQL skills — ability to query, aggregate, model, and interpret large datasets.
• Strong Python skills (pandas, numpy, data modeling, exploratory analysis).
• Solid understanding of statistics, experimentation, time series, optimization techniques, or benchmarking .
• Experience working with large-scale datasets from data warehouses, cloud environments, or analytics platforms.
• Ability to translate complex data into simple unit economics and business insights.
• Experience preparing clear executive-level narratives, dashboards, or insights reports.
• Strong critical thinking and structured problem solving.
• Experience presenting findings to non-technical stakeholders.
• Ability to break complex concepts into simple explanations.
• Strong ownership mentality, curiosity, and willingness to learn specialized Snowflake tooling.
• Quant-driven and intellectually rigorous
• Strong appetite for exploring complex datasets
• Exceptional SQL + Python hands-on ability
• Highly structured in thinking and communication
• Curiosity about cloud platforms and data engineering
• Thrives in analytical ambiguity
• Passionate about driving customer impact with data

Nice To Haves

• Snowflake workload architecture
• FinOps principles and cloud economics
• Cloud computing cost models (AWS/GCP/Azure)
• Query performance tuning concepts
• Data platform performance engineering

Responsibilities

• Analyze large-scale consumption, workload, and performance datasets to uncover insights, trends, and optimization opportunities.
• Build unit economic models such as: cost per query cost per TB scanned cost per user efficiency benchmarks across workloads
• Explore and interpret internal metadata pipelines created by Product/Data Science teams to understand compute, storage, and pipeline behaviors.
• Translate quantitative findings into clear, actionable insights that help customers reduce waste and improve ROI.
• Develop reusable analytical frameworks for consumption modeling and workload optimization.
• Partner with account teams, Sales Engineers, and Value Engineers to support customer conversations with data-backed recommendations.
• Build customer-facing deliverables using Jupyter notebooks, dashboards, and executive-ready PowerPoint narratives.
• Continuously refine analytical approaches to improve accuracy, benchmarking quality, and scale of FinOps engagements.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed