December 2025
This report analyzes 1,580 Data & Analytics job postings from 680+ companies tracked via direct employer career pages and job board aggregators. Our coverage skews toward tech-forward and scaling companies; large enterprises using enterprise hiring platforms may be underrepresented. Coverage varies by section and is noted throughout.
77% of roles with industry data
Fintech leads at 15%, reflecting San Francisco's concentration of financial technology companies from established players like Block and Plaid to emerging challengers. The combined AI/ML (12%) and Data Infrastructure (6%) segments total 18%, underscoring how data tooling and AI development have become industries in their own right. Mobility & Transportation (11%) is driven primarily by autonomous vehicle companies like Waymo and Zoox, which require massive data teams for perception, prediction, and decision-making systems. The relatively modest Healthcare & Biotech share (7%) suggests SF's data talent flows primarily toward consumer and enterprise technology.
76% of roles with company age data
Growth-stage companies founded between 2010-2019 lead hiring at 56%, representing the startup cohort that scaled successfully through the 2010s tech boom. These include companies like Databricks, Roblox, and Instacart that now employ thousands but retain startup DNA and equity upside. Mature companies (30%) provide stability and often larger data platforms to work with. Young startups (14%) offer the highest risk-reward profile - smaller teams mean more ownership but less job security. This distribution suggests the SF data market has matured past its founding generation, with mid-stage companies now driving the majority of hiring.
76% of roles with ownership data
Private companies represent 61% of hiring, reflecting the Bay Area's venture-backed ecosystem. Many of these are late-stage privates like Databricks, OpenAI, and Stripe that offer competitive compensation while retaining equity upside potential. Public companies (29%) include household names like Pinterest, Reddit, and Roblox that provide liquidity and stability. The minimal acquired company presence (0%) suggests that data teams at acquired companies either integrate into parent organizations or experience headcount reductions. The 9% subsidiary share includes companies like Capital One's tech operations that benefit from corporate backing while maintaining startup-like cultures.
76% of roles with company size data
The relatively even distribution across company sizes - 39% Enterprise, 33% Scale-up, 28% Startup - gives candidates meaningful choice in work environment. Enterprise employers offer established data platforms, clear career ladders, and comprehensive benefits. Scale-ups provide the sweet spot of meaningful ownership with reduced early-stage risk. Startups demand generalist capabilities but offer outsized impact and equity potential. Notably, the startup share (28%) remains substantial despite the challenging funding environment, indicating continued investment in data capabilities even among smaller companies.
Large employers with proprietary career sites (Amazon, Google, Meta, Microsoft) may be underrepresented, as our sources primarily capture roles posted through standard direct employer platforms and job aggregators. We are actively expanding our integrations.
Market interpretation: Waymo's market-leading 3% share reflects the data intensity of autonomous vehicle development - perception, prediction, and planning systems require massive ML teams. The presence of two AV companies (Waymo, Zoox) in the top 10 underscores this sector's talent appetite. OpenAI's inclusion underscores SF's role as the center of the generative AI revolution. The top 15 employers among tracked direct employer platforms collectively account for 23% of hiring, with mid-size companies offering substantial opportunity. Fintech representation (Capital One, Block, Plaid, Airwallex) demonstrates that financial services remain a consistent source of data roles.
The 36% ML Engineer concentration is a notable characteristic of SF's data market - more than triple the Data Analyst share (7%). This reflects the Bay Area's shift from analytics-driven decision making to AI-powered product development. Combined with Data Scientists (20%) and Research Scientists (3%), ML-focused roles represent 59% of the market. Data Engineers (19%) remain critical for building the pipelines that feed ML systems. The relatively small Analytics Engineer (4%) and Data Analyst (7%) segments suggest that traditional BI and reporting work increasingly flows to other markets or becomes automated. Product Analytics (6%) maintains relevance as the bridge between data teams and product decisions.
Junior: 0-2 years | Mid-Level: 3-5 years | Senior: 6-10 years | Staff/Principal: 11+ years (IC track) | Director+: Management track
Senior-to-Junior Ratio
21:1
Senior+ roles per Junior role
Entry Accessibility Rate
13%
Junior + Mid-Level roles combined
The 21:1 senior-to-junior ratio creates one of the most experience-demanding markets globally. Senior roles (58%) lead, with Staff/Principal (22%) representing strong demand for technical leaders who can architect complex systems and mentor teams. The 4% junior allocation suggests that SF employers largely expect to hire trained professionals rather than develop them - a market dynamic that pushes early-career talent toward other cities or industries. Mid-level positions (9%) offer a narrow pathway between entry and senior levels. Director-plus roles (7%) indicate healthy leadership opportunities for those who advance through the technical ranks. Note: The ratio reflects all senior-level roles (Senior + Staff/Principal + Director+) divided by Junior positions.
The 92% IC concentration reflects the technical nature of SF's data work and the industry's preference for flat organizational structures. With complex ML systems requiring deep individual expertise, companies invest heavily in technical tracks over management hierarchies. The 8% management share includes data team leads, analytics managers, and directors of data science. This ratio suggests that career advancement in SF's data market flows primarily through technical excellence (Staff/Principal track) rather than people management. Candidates seeking management careers may find more opportunity in enterprise-focused markets or industries with larger, more traditional organizational structures.
Onsite: office full-time | Hybrid: mix of office and remote | Remote: work from anywhere | Flexible: employee chooses arrangement
56% of roles with known working arrangement
Remote work leads at 44%, making SF's premium compensation accessible from anywhere - though this also means global competition for positions. Only 24% of roles require full-time office presence, typically for positions involving sensitive data, hardware integration, or real-time collaboration needs. The combined 76% flexibility rate (remote + hybrid + flexible) reflects the tech industry's post-pandemic normalization of distributed work. Companies like Waymo and Zoox that require physical presence for vehicle testing likely account for much of the onsite share. Hybrid arrangements (23%) often translate to 2-3 days per week in office, concentrated in SF's SOMA and Mission districts.
34% of roles with disclosed salary ranges
25th Percentile
$172K
Median
$205K
75th Percentile
$250K
IQR (Spread)
$78K
84% of roles with skills data
Skills insight: Python leads at 53%, reinforcing its status as the universal language of data and ML. SQL remains essential at 40%, even as the market shifts toward ML - data access and transformation remain foundational. The explicit Machine Learning (21%) and AI (13%) demand reflects role requirements beyond general Python proficiency. PyTorch (11%) outpaces TensorFlow (not in top 15), reflecting the research community's framework preference flowing into production. LLMs appearing at 9% represents the generative AI wave hitting job requirements. The dbt (9%) + Snowflake (9%) combination indicates modern data stack adoption. C++ at 7% reflects performance-critical ML systems, particularly in autonomous vehicles and real-time applications. The Python + SQL pairing (31%) remains the most fundamental skill combination.
2.32
Jobs per employer
Average open roles per hiring company
Among tracked employers
12%
Top 5 concentration
Combined market share of top 5 employers
Among tracked employers
23%
Top 15 concentration
Combined market share of top 15 employers
Among tracked employers
21:1
Senior-to-Junior ratio
Senior+ roles for every junior role
Extremely competitive entry
13%
Entry accessibility
Junior + Mid-Level roles combined (typically <3 years experience required)
Difficult entry market
8%
Management opportunity
Roles on the people management track
Highly IC-focused
44%
Remote availability
Roles offering fully remote work
High flexibility
This report analyzes direct employer job postings for Data & Analytics roles in San Francisco during December 2025.
85%
Seniority coverage
Roles with seniority level classified
56%
Arrangement coverage
Roles with working arrangement known
84%
Skills coverage
Roles with skills extracted from description
77%
Employer metadata
Roles with enriched company data
This report was created by Rich Jacobs, Data product manager focused on hiring market intelligence. Want the data? rich@richjacobs.me