San Francisco Data & Analytics: Python, SQL, Machine Learning Lead 989 Roles -- January 2026

Beta

San Francisco data hiring in January 2026 featured 1,011 roles from 483 employers, with Fintech leading at 17% and strong ML Engineer demand at 40% of positions. Median disclosed salary reached $204k among the 22% of tracked roles with employer-provided salary ranges.

This report analyzes 989 Data & Analytics job postings from 472+ 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.

Key Takeaways for Job Seekers

1.0Target ML-focused roles With 40% of positions being ML Engineer roles, candidates with PyTorch, TensorFlow, and LLM experience are well-positioned in this market.

1.1Prioritize Python and SQL Python (52%) and SQL (34%) remain the most demanded skills, with their combination appearing in 27% of multi-skill requirements.

1.2Explore Fintech opportunities Fintech's 17% market share and growing momentum makes it a promising sector for data professionals seeking stability.

1.3Leverage remote flexibility With 46% fully remote and 90% flexible arrangements, candidates can negotiate location flexibility as a standard expectation.

1.4Research compensation benchmarks 22% of tracked roles disclose employer-provided salary ranges. Among those postings, the median sits at $204k, which may serve as a useful reference point for negotiation.

Skills Demand

57% of roles with skills data

Low

High

Skills insight: Python leads at 52% of roles with skill data, with SQL at 34%. Cloud platforms AWS (17%) and GCP (9%) are well-represented. ML-specific tools like PyTorch (12%), LLMs (13%), and TensorFlow (9%) reflect the market's ML Engineer focus. The Python + SQL combination appears in 27% of multi-skill requirements.

Seniority Distribution

Junior: 0-2 years | Mid-Level: 3-5 years | Senior: 6-10 years | Staff/Principal: 11+ years (IC track) | Director+: Management track

Low

High

vs December 2025

Biggest Gainer

Director+

+3pp

Biggest Decline

Senior

-5pp

Senior-to-Junior Ratio

15:1

Senior+ roles per Junior role

Entry Accessibility Rate

15%

Junior + Mid-Level roles combined

Senior roles represent 52% of positions (-5pp MoM), with Staff/Principal at 22%. Director+ roles grew 4 percentage points to 11%. The 15:1 senior-to-junior ratio indicates a competitive market for entry-level candidates, with only 15% of roles accessible to those with under 3 years experience.

Working Arrangement

Onsite: office full-time | Hybrid: mix of office and remote | Remote: work from anywhere | Flexible: employee chooses arrangement

94% of roles with known working arrangement

45%Remote

Remote45%

Hybrid29%

Flexible16%

Onsite10%

Remote roles lead at 46%, with hybrid at 28% and flexible arrangements at 16%. Only 10% of positions require full onsite presence. This 90% flexibility rate aligns with San Francisco's tech culture and may help employers compete for talent in the current market.

Role Specialization

Low

High

vs December 2025

Biggest Gainer

ML Engineer

+3pp

Biggest Decline

Data Engineer

-2pp

ML Engineer roles lead at 40% (+3pp MoM), reflecting San Francisco's AI/ML focus. Data Scientist follows at 19%, with Data Engineer at 16% (-2pp MoM). Traditional analytics roles (Data Analyst, Product Analytics) comprise a smaller share, suggesting the market skews toward technical implementation over analysis.

IC vs Management Track

89%IC

Individual Contributor89%

Management11%

Individual contributor roles account for 89%, with management positions at 11%. This reflects the technical nature of data work, where hands-on IC roles remain the primary career path for most practitioners.

Compensation

22% of roles with disclosed salary ranges

Overall Distribution

25th Percentile

$165K

Median

$204K

75th Percentile

$243K

IQR (Spread)

$78K

Advertised Salary by Seniority

Advertised Salary by Role

Market Context

1.Elevated US layoffs January 2026 saw the highest US tech layoffs since 2009, which may have increased candidate supply and affected employer hiring patterns.

2.California pay transparency California's salary transparency law requires disclosure, though only 22% of tracked roles include employer-provided salary ranges in this dataset. The lower coverage likely reflects the removal of predicted salary data from aggregator sources.

3.AI/ML cluster strength San Francisco's concentration of AI and machine learning companies is reflected in the 9% industry share and 40% ML Engineer role share.

4.Fintech momentum Fintech's leading 17% industry share and 3 percentage point month-over-month gain suggests continued investment in financial technology talent.

5.Remote work normalization With 46% fully remote and 90% flexible arrangements, San Francisco employers have largely embraced distributed work models.

Methodology

This report analyzes direct employer job postings for Data & Analytics roles in San Francisco during January 2026.

Data collection:

1.Over 1,000 roles from 483+ employers aggregated from multiple sources
2.Recruitment agency postings identified and excluded (4% of raw data)
3.Jobs deduplicated across sources to avoid double-counting

Classification:

1.Roles classified using an LLM-powered taxonomy
2.Subfamily, seniority, skills, and working arrangement extracted
3.Employer metadata enriched from company databases where available

Limitations:

1.Not a complete census of the market - some roles may not be captured
2.Skills analysis based on 560 roles with skill data (57% coverage)
3.Salary data available due to pay transparency law
4.Working arrangement based on 296 ATS-sourced roles (Adzuna excluded due to truncated descriptions)

Data coverage:

77%

Seniority coverage

Roles with seniority level classified

94%

Arrangement coverage

Roles with working arrangement known

57%

Skills coverage

Roles with skills extracted from description

73%

Employer metadata

Roles with enriched company data

Want interactive dashboards and the full dataset?