Please give me a summary comparison across Snowflake and Databricks.
LinkedIn Talent Data Insights
On the workforce metrics we track, Databricks shows the stronger signal on every dimension — faster headcount growth, better retention, more aggressive hiring, higher engineering density.
Growth. Databricks grew headcount +33% YoY against Snowflake's +16%. Databricks is scaling roughly twice as fast, from a larger base (14,374 vs 10,790 employees).
Retention. Databricks attrition is 9%, Snowflake is 16%. Snowflake is losing close to 1 in 6 employees annually; Databricks loses about 1 in 11. The 7-point attrition delta translates to a structurally different rate of replacement hiring at Snowflake — at 16% on a 10,790 base, replacement is roughly 1,725/year vs Databricks's roughly 1,294/year on a 14,374 base.
Hiring intent. Databricks has 934 open jobs against 14,374 employees — a 6.5% open rate. Snowflake has 523 open against 10,790 — 4.8%. Databricks is signaling a more aggressive 12-month build.
Engineering density. 43% of Databricks' workforce sits in Engineering, versus 37% at Snowflake. In absolute terms, that's 6,181 engineers at Databricks against 3,992 at Snowflake — a 55% larger engineering org on a 33% larger workforce.
To put these numbers in category context, we scored both companies against MongoDB, Oracle, and Confluent on our Talent Moat Score — a 100-point composite weighting Acquisition, Retention, Skills momentum, Hiring Intent, and Pedigree equally (20 points each). Each sub-score is anchored to a defensible benchmark: Retention scales linearly off attrition rate, Skills scales off the average growth of each company's top-5 fastest-growing skills, Acquisition off net inflow per 1,000 employees, Intent off open jobs as % of headcount, and Pedigree off elite-CS school concentration. Full breakdown below.
On the composite, Databricks sits at 85. Snowflake scores 61, edging Oracle (60) and MongoDB (59), with Confluent at 53. Databricks sits in a tier of its own; Snowflake, Oracle, MongoDB and Confluent all cluster within a 10-point band rather than separating upward with Databricks.
To anchor what 61 vs 85 actually mean in the broader Lumen dataset, here's how each maps against companies outside data infrastructure.
The Snowflake band (scores 60-66) is crowded and cross-industry. Costco scores 66 — driven by 4% attrition and 25,954 open roles, retention-led rather than acquisition-led. Kroger 64 and Establishment Labs 64 land in the same range for very different reasons (low-attrition workforce stability vs. aggressive medtech hiring intent). Apple 62, HubSpot 62, and Roku 62 all sit one point above Snowflake. BCG 63 and Bain 61 — the strongest of the MBB cohort — also fall here. Fox Factory 60 matches Oracle. The read: a 61 puts Snowflake in the company of operationally solid, well-regarded employers across retail, consumer tech, SaaS, medtech, and consulting — competitive but not category-defining on the workforce signal.
The Databricks band (scores 78+) is sparse and concentrated in frontier AI and accelerated compute. Across the Lumen dataset, only three other companies clear 78: Anthropic 94, OpenAI 84, and NVIDIA 79. There is no consumer, retail, SaaS, medtech, or consulting name in this tier — the next-closest non-AI/non-GPU score is Google at 70. Databricks at 85 sits between OpenAI and Anthropic on the composite, and is the only data-infrastructure company in our coverage to break into it. On the Talent Moat composite, Databricks's nearest neighbors are frontier-model labs and NVIDIA rather than Snowflake's peer set.
Databricks is winning the direct trade between the two — 43 Snowflake employees moved to Databricks over the last 12 months, against 31 moving the other way (net +12 to Databricks).
Headline totals (LinkedIn, last 12 months): - *Snowflake:* 3,317 hires / 1,675 departures / +1,642 net change. 10,790 employees on LinkedIn. - *Databricks:* 5,011 hires / 1,168 departures / +3,843 net change. 14,374 employees on LinkedIn.
Databricks is hiring at roughly 1.5x Snowflake's rate (5,011 vs 3,317), with materially lower attrition (1,168 vs 1,675 departures). Net headcount addition at Databricks (+3,843) is more than 2x Snowflake's (+1,642).
Direct flow between the two (12mo). 43 Snowflake employees moved to Databricks; 31 Databricks employees moved to Snowflake. Net +12 to Databricks.
The category cohort context. AWS, Google, and Microsoft are the three largest counterparties for both companies — by an order of magnitude. AWS alone delivered 398 hires to Databricks and 184 to Snowflake. The two companies are competing for the same pool of cloud-data engineering and customer-engineering talent, and Databricks is pulling more of it.
Anthropic is the largest single non-hyperscaler outflow destination for both. Snowflake lost 34 employees to Anthropic; Databricks lost 40. Plus OpenAI 32 from Databricks and 24 from Snowflake. The frontier-model labs are the largest captured destinations for departing talent at both companies outside of Big Tech / hyperscalers.
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Analyst
Volume aside — how does the quality of the talent each is hiring compare?
LinkedIn Talent Data Insights
Quality reads on two axes: where engineers were trained, and where they're coming from. Specific numbers on both.
Schools. Databricks' top 5 employee schools contribute 1,100 employees — 325 from UC Berkeley, 231 from Carnegie Mellon, 190 from Georgia Tech, 188 from Stanford, 166 from UIUC. All five are top-10 US CS programs. Snowflake's top 5 contribute 695 employees — 202 Berkeley, 130 U. Washington, 125 USC, 123 San José State, 115 Waterloo. Three of those (Berkeley, UW, Waterloo) sit in the elite CS tier; USC and San José State are solid regional programs but don't feed the same FAANG/AI-lab recruiting funnel.
Net, the elite-CS employee base is 1,100 at Databricks vs 447 at Snowflake — 2.5x. Normalized to workforce size (14,374 vs 10,790), Databricks has 77 employees per 1,000 from top-5 schools vs Snowflake's 64, with the concentration weighted more tightly at the top tier.
Inflow sources. Databricks' external inflows are dominated by hyperscaler engineering: AWS 398 gross, Google +175 net, Microsoft +135 net. Snowflake's hyperscaler inflows pull from the same set at roughly half the volume: AWS 184 gross, Microsoft +63 net, Google +40 net. Same recruiting pool, different draw.
What this means for the talent picture. Two pools, different shapes. Databricks is sourcing technical talent from a wider top-tier school footprint and pulling more volume from the same hyperscalers Snowflake recruits from. The two recruiting funnels are drawing the same source water at different rates — Databricks's 2.5x advantage in elite-CS employee base and 2-4x advantage in hyperscaler inflow are both visible in the captured data.
Top 5 Employee Schools — both draw from top CS programs; Databricks has more elite-tier concentration
#
Snowflake
Databricks
1
UC Berkeley (202)
UC Berkeley (325)
2
U. of Washington (130)
Carnegie Mellon (231)
3
USC (125)
Georgia Tech (190)
4
San José State (123)
Stanford (188)
5
Waterloo (115)
UIUC (166)
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Analyst
What does the growing-skills data signal about each company's strategic direction?
LinkedIn Talent Data Insights
The growing skills data shows what each company is investing in, and the skill mixes are materially different.
Snowflake's growing skills are AI/ML on top of cloud data infrastructure. Applied Machine Learning (+34%), Deep Learning (+33%), Natural Language Processing (+31%), Amazon EC2 (+30%), Elasticsearch (+29%). The capability set maps to adding AI features into a data warehouse architecture — ML engineers, cloud infrastructure, and search/retrieval primitives.
Databricks' growing skills are platform infrastructure and multimodal AI. AWS Lambda (+41%), Terraform (+39%), Docker (+38%), Computer Vision (+38%), Git (+38%). The capability set maps to operating a horizontal platform — infrastructure engineering, DevOps, and computer vision — at scale across diverse use cases.
What the skill mix maps to operationally. Snowflake's growing skills (Applied ML, Deep Learning, NLP, EC2, Elasticsearch) are the capability set required to add AI features to an existing data-warehouse architecture and customer base. Databricks' growing skills (Lambda, Terraform, Docker, Computer Vision, Git) are the capability set required to operate a horizontal platform across infrastructure, ML, and multimodal use cases.
The captured signal. The two companies are hiring for different operating profiles — Databricks at +38% growth in Computer Vision and Terraform, Snowflake in Applied ML and Elasticsearch. The workforce data shows the direction; revenue follows whichever bet the enterprise buyer validates over the next 12-24 months.
Snowflake — Top 5 fastest-growing skills (YoY %)
Applied ML
34%
Deep Learning
33%
NLP
31%
Amazon EC2
30%
Elasticsearch
29%
Databricks — Top 5 fastest-growing skills (YoY %)
AWS Lambda
41%
Terraform
39%
Docker
38%
Computer Vision
38%
Git
38%
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Analyst
How do they compare against MongoDB, Oracle, and Confluent?
LinkedIn Talent Data Insights
The three comparators each tell a different story, and putting them next to Snowflake and Databricks gives you the category shape.
MongoDB. 7,400 employees, -5% YoY (workforce shrinking), 16% attrition. The growing skills are the most AI-native in the category: Atlas Vector Search +58%, Generative AI +45%, LLM Integration +42%, Kubernetes +28%, Rust +22%. The roadmap build is pointed squarely at vector databases and RAG infrastructure. At the same time, MongoDB is losing 45 people net to Databricks and 35 net to Snowflake — the talent building the AI capability is flowing out to larger peers. Whether the workforce stabilizes is the open question.
Oracle. 150,000 employees, +2% YoY, 12% attrition. Net losses of 380 to AWS and 140 to Microsoft in the 12-month window. Oracle Cloud Infrastructure shows up as the +45% fastest-growing skill — they're building the capability, but the outflow to AWS/Azure is running in parallel. Worth noting: MongoDB pulled 85 hires from Oracle, Confluent pulled 45. The modern data-infra category is sourcing meaningfully from Oracle.
Confluent. 3,689 employees, -10% YoY (the steepest contraction in the group), 18% attrition (the highest). Growing skills are technically coherent — Apache Flink +42%, Data Streaming +38% — but net -28 to Databricks and a shrinking base put the talent flywheel in reverse.
The category read as questions. Is data infrastructure consolidating around Databricks, or does MongoDB's AI-native skill build hold enough ground for a three-winner outcome? Can Snowflake's customer base offset a narrower bench over 24-36 months? How much longer does Oracle remain a net talent source for the modern stack before OCI execution feels the drain? Is Confluent's contraction cyclical or structural? The workforce data raises each; answers take more than one quarter of signal.
Data Infrastructure Category — Talent Health Dashboard
Databricks
Healthy
Growth+33%
Attrition9%
Moat Score85/100
Leads on growth, retention, and skill build
Snowflake
Watch
Growth+16%
Attrition16%
Moat Score61/100
+16% growth on real ML build; 16% attrition runs above Databricks
MongoDB
Watch
Growth-5%
Attrition16%
AI Skills+58% Vector
Most AI-native skill build in cohort; retention 16%, headcount -5% YoY
Oracle
Watch
Growth+2%
Attrition12%
Net Cloud Loss-520 to AWS/MS
Net -520 to AWS/Microsoft; OCI skill build in parallel
Confluent
Alert
Growth-10%
Attrition18%
DBX Loss-28 net
Headcount -10% YoY; 18% attrition; net -28 to Databricks
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Analyst
What's the talent data saying across SNOW, DBX, and the comparators?
LinkedIn Talent Data Insights
On the workforce signals, here's what's visible:
Databricks leads on every captured metric in this view. 9% attrition, +33% headcount growth, 43% engineering density, 1,100 employees from top-5 US CS programs, and hyperscaler inflows running 2-4x Snowflake's on an organic basis. The skill growth is in infrastructure and multimodal AI (AWS Lambda +41%, Terraform +39%, Computer Vision +38%) — a workforce being built for platform breadth.
Snowflake is still growing — +16% headcount, a capable Bay Area engineering bench, a real AI-skills build (Applied ML +34%, Deep Learning +33%). The gap shows up in attrition (16% vs 9%) and in the narrower inflow base when Observe is stripped as an acquisition. Half of annual hiring goes to replacement. The skills direction is ML-on-warehouse rather than platform-breadth.
MongoDB shows a split signal. The workforce is shrinking (-5% YoY), and 16% attrition is in the same band as Snowflake. The skill build is the most AI-native in the category — Atlas Vector Search +58%, Generative AI +45%, LLM Integration +42%. The retention trajectory determines whether the AI-native skill base translates into a stable workforce foundation.
Oracle remains a net donor to AWS, Microsoft, and the modern data-infra category. OCI is their +45% growing skill, but the outflows to hyperscalers (-520 net captured) are structural.
Confluent has 18% attrition against -10% growth — the workforce signature of a company shrinking faster than its talent base can stabilize.
Questions the talent data raises for the investor. Does Databricks' widening workforce advantage translate into durable product velocity over a 24-36 month horizon, or does Snowflake's customer base and ARR engine offset the bench gap? Is MongoDB's AI-native skill build enough to attract net-new talent inflow, or will the outflow to Databricks/Snowflake keep compounding? How much of Oracle's talent reservoir has to drain before OCI execution slows materially?
Workforce signals at a glance — Databricks leads on every captured metric; the rest of the category is running three different plays
Company
Talent Moat
HC Growth
Attrition
Workforce signal
Databricks
85/100
+33%
9%
Widening advantage across growth, retention, and skill build
Snowflake
61/100
+16%
16%
Real ML build; half of hiring is replacement; narrower bench
MongoDB
59/100
-5%
16%
Most AI-native skill build; retention is the swing factor
Oracle
60/100
+2%
12%
Net talent donor to AWS/Azure; OCI skill build in parallel
Confluent
53/100
-10%
18%
Workforce contracting faster than the bench can stabilize
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Analyst
What does the Open-to-Work signal look like for Snowflake and Databricks?
LinkedIn Talent Data Insights
LinkedIn Recruiter's Open-to-Work data captures members who have flagged themselves as open to new roles. We pull this two ways: company-wide (the headline OTW rate) and by skill (where the OTW signal concentrates).
Headline OTW rate. Snowflake 23% (2,500 of 10,790); Databricks 24% (3,400 of 14,374). Both companies sit in the high-20s — typical of large software companies in the current tech labor market. The Databricks rate runs slightly above Snowflake's despite Databricks's 9% blended attrition vs Snowflake's 16% — likely a permissive 'open to opportunities' read on Databricks's side rather than active intent.
Active talent. The stricter signal — members showing recent application or recruiter-engagement activity. Snowflake 13% (1,400); Databricks 8% (1,200). Both track close to their respective blended attrition rates, suggesting the Active-talent signal is correlated with realized turnover rather than running ahead of it.
By-skill view (where the OTW signal concentrates). Across the 8 reliable skill keywords we track — Software Development, Machine Learning, Product Management, Sales, Marketing, Customer Success, Operations Management, Financial Analysis — both companies show OTW rates in the 21-28% band with no single function dominating. Full breakdown below.
Snowflake Open-to-Work Aggregate (LinkedIn Recruiter, April 30 2026 data)
10,790
Employees on LinkedIn
Total Snowflake captured profiles
2,500
Open to Work
23% of captured profiles
1,400
Active talent
13% — actively job-searching
343
Rediscovered candidates
Engaged with recruiters previously
Databricks Open-to-Work Aggregate (LinkedIn Recruiter, April 30 2026 data)
14,374
Employees on LinkedIn
Total Databricks captured profiles
3,400
Open to Work
24% of captured profiles
1,200
Active talent
8% — actively job-searching
133
Rediscovered candidates
Engaged with recruiters previously
Snowflake Open-to-Work by Skill (LinkedIn Recruiter, April 30 2026 data)
Skill
Captured
Open to Work
% OTW
Active talent
% Active
Software Development
7,700
1,800
23%
1,000
13%
Machine Learning
2,400
653
27%
404
17%
Product Management
3,700
933
25%
610
16%
Sales
4,900
1,100
22%
854
17%
Marketing
3,600
921
26%
640
18%
Customer Success
1,600
415
26%
320
20%
Operations Management
3,000
798
27%
557
19%
Financial Analysis
1,900
533
28%
357
19%
Databricks Open-to-Work by Skill (LinkedIn Recruiter, April 30 2026 data)
Skill
Captured
Open to Work
% OTW
Active talent
% Active
Software Development
11,000
2,500
23%
922
8%
Machine Learning
4,700
962
20%
556
12%
Product Management
4,200
870
21%
497
12%
Sales
5,200
1,000
19%
658
13%
Marketing
3,800
801
21%
484
13%
Customer Success
1,800
419
23%
286
16%
Operations Management
3,300
699
21%
443
13%
Financial Analysis
2,400
503
21%
295
12%
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All workforce data captured from LinkedIn Talent Insights and LinkedIn Recruiter, April 2026.
Captured profiles are LinkedIn-visible employees tagged to a company; this set typically exceeds active headcount because recent ex-employees may still list the company on their profile.
Open-to-Work is LinkedIn's signal where members flag themselves as open to new roles. Reported as a percentage of captured profiles.
Talent Moat Score is a 100-point composite weighting Acquisition, Retention, Skills momentum, Hiring Intent, and Pedigree at 20 points each. Scores are anchored to category benchmarks across the Lumen dataset.