Why This Job is Featured on The SaaS Jobs
### Why this Role is Featured on The SaaS Jobs
FinOps and growth analytics sit at the center of modern SaaS economics, especially for cloud-native platforms where consumption patterns directly shape margins and customer outcomes. This Value Engineer role is notable because it connects product usage telemetry to commercial decision-making, using data from real workloads rather than abstract benchmarks—an increasingly important capability across usage-based and hybrid SaaS models.
Long-term, the work builds a durable SaaS skill set: translating cloud consumption into unit economics, forming optimization narratives, and packaging insights for both technical and executive audiences. The mix of SQL/Python analysis, modeling, and stakeholder-facing deliverables mirrors how many SaaS companies operationalize analytics for retention, expansion, and efficient scaling. The listing also signals structured learning in platform-specific FinOps concepts, which can broaden career options across data platforms, cloud infrastructure, and analytics-led GTM functions.
The section above is editorial commentary from The SaaS Jobs, provided to help SaaS professionals understand the role in a broader industry context.
Job Description
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.
KEY RESPONSIBILITIES
Analyze large-scale consumption, workload, and performance datasets to uncover insights, trends, and optimization opportunities.
Build unit economic models such as:
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.
REQUIRED SKILLS
Quantitative & Technical
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.
Analytical Thinking & Insights Storytelling
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.
Communication & Collaboration
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.
PREFERRED SKILLS
These areas will be taught on the job:
Snowflake workload architecture
FinOps principles and cloud economics
Cloud computing cost models (AWS/GCP/Azure)
Query performance tuning concepts
Data platform performance engineering
If you don't know these today, you will learn them here.
Ideal Candidate Traits
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
Every Snowflake employee is expected to follow the company’s confidentiality and security standards for handling sensitive data. Snowflake employees must abide by the company’s data security plan as an essential part of their duties. It is every employee's duty to keep customer information secure and confidential.
Snowflake is growing fast, and we’re scaling our team to help enable and accelerate our growth. We are looking for people who share our values, challenge ordinary thinking, and push the pace of innovation while building a future for themselves and Snowflake.
How do you want to make your impact?
For jobs located in the United States, please visit the job posting on the Snowflake Careers Site for salary and benefits information: careers.snowflake.com
