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The Missing Third Leg of Every Agentic AI Stack
Every architecture diagram I see draws the same three boxes. Sometimes it's Model + RAG + MCP. Sometimes it's Model + MCP + Tools. The labels move around, but the shape never changes. The diagram describes how an agent reaches your data. It says nothing a
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The Multi-Cloud Metrics Problem Snowflake, Databricks, and BigQuery Can't Solve Alone
Enterprises running Snowflake, Databricks, and BigQuery side by side no longer struggle with data movement. Data pipeline tools solved that years ago. Now they have a metrics problem: the same "gross margin" or "active users" number means something differ
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Inside a Real Benchmark: Measuring the Cost of AI on the Warehouse
Any AI performance conversation runs the same three tracks: token costs, inference latency, and model accuracy, and all three numbers matter. But once an AI system reaches enterprise data, a fourth system starts determining both cost and performance: the
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The Other Half of Intelligence Efficiency
AI usage isn't the same as AI value. That's why Snowflake introduced "intelligence efficiency" this week. In a post on Snowflake's blog, CEO Sridhar Ramaswamy laid out how companies are pouring money into AI, and boards want proof it's translating into mo
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Anatomy of a Vague Natural Language Prompt: What an Unguided LLM Does to Your Warehouse
"What was revenue in the Northeast region last quarter?" At first blush, the query looks simple enough. It reads like something you'd ask an analyst on your team and get an answer back in five minutes. But an LLM can't read an analyst's mind, and it didn'
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Have Meaning, Will Travel: Why Apache Ossie Needs You
Here's a heretical question worth considering: what if standards are actually beside the point for semantic layers? Apache Ossie was recently announced as the open standard successor to the Open Semantic Interchange (OSI) initiative. Its mission is to cre
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AI Isn't Replacing BI. It's Finally Finishing the Job.
Will AI replace BI? Consider this: The cloud didn't kill the data warehouse. Self-service didn't kill the dashboard. Donald Farmer, who's watched decades of this cycle repeat, put it to me plainly on the Data-Driven podcast: every generation of data techn
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Inside the AtScale and Snowflake Partnership: Why Snowflake Embedded AtScale in Semantic Views
The Snowflake Semantic Views XMLA Endpoint, powered by AtScale, is an integrated product offering. It's the only semantic solution to have this distinction. It's not something bolted on the side of Snowflake; it's the bet Snowflake made on AtScale. The so
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Five Questions Every CDO Should Ask About AI and Semantics
The metrics powering your AI agents right now, like margin per square foot or total economic cost, probably don't belong to you. They belong to whichever platform computes them today. Snowflake, Databricks, the frontier LLM labs, Microsoft, Salesforce, Go
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Don't Let Microsoft Be Your Warden
Microsoft just made the case for AtScale by playing Warden Norton, the prison boss from "The Shawshank Redemption." On Tuesday, Sara Vredevoogd from Microsoft's Power BI team announced that Microsoft 365 Copilot can now query Power BI semantic models dire
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Tech stack
Technologies named in job postings at AtScale over the last 12 months, by layer, with the share of postings for each.
Technologies in use
16
Postings analysed
3
Stack modernity
77/100
Languages
Python
SQL
Frameworks & libraries
LlamaIndex
PoC Library
Databases
BigQuery
Google BigQuery
Snowflake
Cloud & platforms
Databricks
AWS
Azure
GCP
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Full AtScale tech stack
16 technologies · by team · pay by technology · changes · similar stacks
People

David Mariani
Director

Chris Lynch
Executive Officer, Director

David Lam
Director

David Zilberman
Director

Kevin Johnson
Director

Ryan Floyd
Director

Roseann Palmieri
Director

Michael Egan
Director

Mark Palmer
Director
Names and roles come from public filings, company registers and the company's own site. Is this you? Correct or remove · How we handle this data
Growth
Hiring Momentum
83/100
Scaling fast
Open positions
3
3 opened / 0 closed in 30 days
Hiring Dynamics
+100%
Hiring Focus
The percentage next to each role is its share of the company's job openings over the last 90 days; the arrow shows the shift versus the previous period.
Solutions
67% ▲
AI/ML
33% ▲
Activity Timeline
Added Python to stack
Oct 2026
Added LlamaIndex to stack
Oct 2026
Added Kubernetes to stack
Oct 2026
Added Git to stack
Oct 2026
Added GCP to stack
Oct 2026
Added Docker to stack
Oct 2026
Added CI/CD to stack
Oct 2026
Added Azure to stack
Oct 2026
Added AWS to stack
Oct 2026
Offices
Where the company hires and what each office is for
Cities
1
Hiring now
0
Countries
1
San Mateo
United States
HQ
Jobs
≈ $113k – $233k per year (Estimated) • Remote (likely United States) • Full-Time • 7+ years exp
Snowflake
Databricks
Google BigQuery
BigQuery
Apply
≈ $105k – $233k per year (Estimated) • Remote (likely United States, UTC-6 – UTC-5 hours) • Full-Time • 10+ years exp
Python
Snowflake
Databricks
Google BigQuery
BigQuery
AI/ML
LlamaIndex
DevOps
GCP
Azure
CI/CD
Git
AWS
Docker
Kubernetes
Apply
≈ $113k – $233k per year (Estimated) • Remote (likely United States) • Full-Time • 5+ years exp
SQL
Snowflake
Databricks
Google BigQuery
BigQuery
AI/ML
AI Agents
Chips/EDA
PoC Library
Apply
