Network Analysis Report

Generated: February 2, 2026
Scope: Network-accepted candidates who are open to opportunities
Total Candidates: 5,702

Executive Summary

The Remotely.works talent network consists of 5,702 candidates who are network-accepted and currently open to opportunities. The network is heavily weighted toward:

Senior engineering talent (IC4/IC5 = 84% of network)
Backend and Fullstack roles (combined 74% of role assignments)
JavaScript/React/AWS ecosystem (top 3 tech strengths)
Latin American talent (Brazil 49%, Argentina 11%, Mexico 10%)

1. Seniority Distribution

Seniority	Count	% of Network
IC4	3,217	56.42%
IC5	1,590	27.88%
IC3	481	8.44%
Unknown	137	2.40%
IC6	133	2.33%
Manager	82	1.44%
IC2	45	0.79%
Lead	8	0.14%
Director	7	0.12%
VP	1	0.02%
Partner	1	0.02%

Key Insight: IC4 (Senior) and IC5 (Staff) comprise 84% of the network. Junior talent (IC2/IC3) is scarce at only 9%.

-- Query: Seniority distribution
SELECT 
    COALESCE(ts_interview->>'seniority', 'unknown') as seniority,
    COUNT(*) as count,
    ROUND(COUNT(*)::numeric * 100 / SUM(COUNT(*)) OVER (), 2) as pct_of_network
FROM candidates 
WHERE status = 'network_accepted' 
  AND open_to_opportunities = true
  AND deleted_at IS NULL
GROUP BY ts_interview->>'seniority'
ORDER BY count DESC;

2. Role Distribution

By Role Kind

Kind	Candidates	% of Network
Engineering	5,091	89.28%
Other	589	10.33%
Design	212	3.72%

Note: Candidates can have multiple roles, so percentages sum to >100%

By Specific Role

Role	Kind	Count	% of Network
Backend Engineer	engineering	2,215	38.85%
Fullstack Engineer	engineering	2,016	35.36%
Frontend Engineer	engineering	930	16.31%
Mobile Engineer	engineering	446	7.82%
QA Engineer	engineering	297	5.21%
Data Engineer	engineering	295	5.17%
DevOps Engineer	engineering	280	4.91%
QA Analyst	other	209	3.67%
Engineering Manager	engineering	197	3.45%
UX/UI Designer	design	166	2.91%
Data/BI Analyst	other	136	2.39%
Machine Learning	engineering	136	2.39%
Solutions Architect	engineering	130	2.28%
Data Scientist	engineering	129	2.26%
Product Designer	design	106	1.86%
Customer Support Engineer	engineering	90	1.58%
Project Manager	other	85	1.49%
Product Manager	other	80	1.40%
Other	engineering	71	1.25%
Customer Success Specialist	other	67	1.18%
Salesforce Engineer	engineering	50	0.88%
Business Analyst	other	20	0.35%
Security Engineer	engineering	20	0.35%
Scrum Master	engineering	19	0.33%
Game Engineer	engineering	14	0.25%
Marketing Analyst	other	13	0.23%
Account Executive	other	5	0.09%
UX Researcher	design	3	0.05%
Release Manager	engineering	3	0.05%
Graphic Designer	design	3	0.05%

Key Insight: Backend + Fullstack = 74% of the network. Design roles (UX/UI, Product Designer, Graphic Designer, UX Researcher) represent only 4.87% combined.

-- Query: Role distribution
SELECT 
    jor.name as role,
    jor.kind,
    COUNT(DISTINCT c.id) as candidate_count,
    ROUND(COUNT(DISTINCT c.id)::numeric * 100 / (
        SELECT COUNT(*) FROM candidates 
        WHERE status = 'network_accepted' AND open_to_opportunities = true AND deleted_at IS NULL
    ), 2) as pct_of_network
FROM candidates c
CROSS JOIN LATERAL unnest(c.roles) as role_id
JOIN job_opening_role jor ON jor.id = role_id
WHERE c.status = 'network_accepted' 
  AND c.open_to_opportunities = true
  AND c.deleted_at IS NULL
  AND jor.deleted_at IS NULL
GROUP BY jor.id, jor.name, jor.kind
ORDER BY candidate_count DESC;

3. Role × Seniority Matrix

Role	IC2	IC3	IC4	IC5	IC6	Leadership	Total
Backend Engineer	9	137	1,267	707	57	10	2,215
Fullstack Engineer	9	150	1,189	591	33	7	2,016
Frontend Engineer	9	83	582	230	8	1	930
Mobile Engineer	1	27	277	121	7	2	446
QA Engineer	0	27	179	81	2	3	297
Data Engineer	4	41	171	65	4	3	295
DevOps Engineer	0	27	137	99	11	1	280
QA Analyst	2	28	111	63	2	3	209
Engineering Manager	0	0	7	73	49	67	197
UX/UI Designer	2	16	97	42	2	3	166
Data/BI Analyst	1	21	84	23	0	1	136
Machine Learning	0	14	76	33	10	1	136
Solutions Architect	0	0	29	73	16	10	130
Data Scientist	0	19	78	23	4	1	129
Product Designer	1	2	48	50	2	2	106
Customer Support Engineer	12	19	47	10	0	2	90
Project Manager	0	10	45	17	3	10	85
Product Manager	0	7	25	19	7	11	80
Other	1	12	25	19	3	7	71
Customer Success Specialist	3	11	42	9	0	0	67
Salesforce Engineer	0	1	27	21	1	0	50
Business Analyst	0	4	12	2	0	1	20
Security Engineer	0	1	6	13	0	0	20
Scrum Master	0	0	8	6	2	1	19
Game Engineer	0	1	8	5	0	0	14
Marketing Analyst	3	2	8	0	0	0	13
Account Executive	0	0	0	0	0	0	5
Release Manager	0	0	1	1	1	0	3
UX Researcher	0	0	2	1	0	0	3
Graphic Designer	0	0	2	1	0	0	3

Leadership includes: lead, manager, director, vp, partner

-- Query: Role × Seniority matrix
SELECT 
    jor.name as role,
    SUM(CASE WHEN ts_interview->>'seniority' = 'IC2' THEN 1 ELSE 0 END) as IC2,
    SUM(CASE WHEN ts_interview->>'seniority' = 'IC3' THEN 1 ELSE 0 END) as IC3,
    SUM(CASE WHEN ts_interview->>'seniority' = 'IC4' THEN 1 ELSE 0 END) as IC4,
    SUM(CASE WHEN ts_interview->>'seniority' = 'IC5' THEN 1 ELSE 0 END) as IC5,
    SUM(CASE WHEN ts_interview->>'seniority' = 'IC6' THEN 1 ELSE 0 END) as IC6,
    SUM(CASE WHEN ts_interview->>'seniority' IN ('lead','manager','director','vp','partner') THEN 1 ELSE 0 END) as leadership,
    COUNT(DISTINCT c.id) as total
FROM candidates c
CROSS JOIN LATERAL unnest(c.roles) as role_id
JOIN job_opening_role jor ON jor.id = role_id
WHERE c.status = 'network_accepted' 
  AND c.open_to_opportunities = true
  AND c.deleted_at IS NULL
  AND jor.deleted_at IS NULL
GROUP BY jor.name
ORDER BY total DESC;

4. Tech Strengths (Top 100)

Based on ts_interview.tech_strengths field:

Rank	Tech Strength	Candidates	% of Network
1	JavaScript	1,958	34.34%
2	AWS	1,830	32.09%
3	React	1,812	31.78%
4	TypeScript	1,498	26.27%
5	Python	1,495	26.22%
6	Node.js	1,290	22.62%
7	Java	1,255	22.01%
8	SQL	985	17.27%
9	PostgreSQL	888	15.57%
10	Docker	828	14.52%
11	C#	648	11.36%
12	MySQL	599	10.51%
13	Kubernetes	587	10.29%
14	MongoDB	546	9.58%
15	.NET	543	9.52%
16	Azure	539	9.45%
17	Angular	516	9.05%
18	Springboot	465	8.16%
19	GCP	436	7.65%
20	Vue.js	413	7.24%
21	Next.js	404	7.09%
22	PHP	399	7.00%
23	Jira	386	6.77%
24	Spring	382	6.70%
25	Kotlin	361	6.33%
26	Terraform	331	5.80%
27	Jenkins	315	5.52%
28	CI/CD	306	5.37%
29	Django	293	5.14%
30	Express	288	5.05%
31	SQL Server	283	4.96%
32	Go (Golang)	279	4.89%
33	CSS	267	4.68%
34	Figma	264	4.63%
35	Angular.js	263	4.61%
36	Postman	251	4.40%
37	GraphQL	248	4.35%
38	Selenium	239	4.19%
39	HTML	228	4.00%
40	Kafka	225	3.95%
41	Ruby on Rails	223	3.91%
42	.NET Core	221	3.88%
43	Laravel	218	3.82%
44	Cypress	205	3.60%
45	Redis	204	3.58%
46	React Native	204	3.58%
47	Oracle Database	198	3.47%
48	Nest.js	192	3.37%
49	Android	191	3.35%
50	Flask	190	3.33%
51	Redux	187	3.28%
52	Swift	183	3.21%
53	Ruby	183	3.21%
54	Git	181	3.17%
55	PowerBI	177	3.10%
56	FastAPI	165	2.89%
57	Linux	163	2.86%
58	Pandas	163	2.86%
59	iOS	156	2.74%
60	Airflow	150	2.63%
61	ASP.Net	147	2.58%
62	Databricks	146	2.56%
63	GitHub Actions	146	2.56%
64	DynamoDB	145	2.54%
65	Playwright	134	2.35%
66	ETL	134	2.35%
67	REST	123	2.16%
68	SCRUM	122	2.14%
69	Azure DevOps	122	2.14%
70	Confluence	120	2.10%
71	Jest	119	2.09%
72	Github	117	2.05%
73	AWS Lambda	115	2.02%
74	SalesForce	113	1.98%
75	Grafana	112	1.96%
76	NumPy	111	1.95%
77	LLMs	111	1.95%
78	Tableau	109	1.91%
79	Tensorflow	109	1.91%
80	Rest API	108	1.89%
81	Snowflake	108	1.89%
82	PySpark	103	1.81%
83	Microservices	102	1.79%
84	Datadog	98	1.72%
85	C++	98	1.72%
86	ElasticSearch	98	1.72%
87	BigQuery	93	1.63%
88	PyTorch	93	1.63%
89	SwiftUI	88	1.54%
90	RabbitMQ	87	1.53%
91	Miro	87	1.53%
92	Ansible	87	1.53%
93	Flutter	85	1.49%
94	Agile Methodology	81	1.42%
95	Bash	81	1.42%
96	Firebase	81	1.42%
97	Apache Spark	80	1.40%
98	Scala	79	1.39%
99	scikit-learn	78	1.37%
100	Objective-C	75	1.32%

Key Insight: The network is dominated by the JavaScript/React ecosystem (34% JavaScript, 32% React) and cloud infrastructure (32% AWS, 9% Azure, 8% GCP). Python is strong at 26%, while Go is relatively underrepresented at 5%.

-- Query: Tech strengths distribution
SELECT 
    t.name as tech_strength,
    COUNT(DISTINCT c.id) as candidate_count,
    ROUND(COUNT(DISTINCT c.id)::numeric * 100 / (
        SELECT COUNT(*) FROM candidates 
        WHERE status = 'network_accepted' AND open_to_opportunities = true AND deleted_at IS NULL
    ), 2) as pct_of_network
FROM candidates c
CROSS JOIN LATERAL jsonb_array_elements_text(c.ts_interview->'tech_strengths') as strength_id
JOIN tags t ON t.id = strength_id::int
WHERE c.status = 'network_accepted' 
  AND c.open_to_opportunities = true
  AND c.deleted_at IS NULL
  AND t.deleted_at IS NULL
GROUP BY t.id, t.name
ORDER BY candidate_count DESC;

5. Experience Distribution

Experience Bucket	Count	% of Network
0-3 years	410	7.19%
4-5 years	658	11.54%
6-8 years	1,551	27.20%
9-10 years	1,055	18.50%
11-15 years	1,314	23.04%
15+ years	714	12.52%

Key Insight: 81% of candidates have 6+ years of experience. Early-career talent (0-5 years) represents only 19%.

-- Query: Experience distribution
SELECT 
    CASE 
        WHEN experience_years <= 3 THEN '0-3 years'
        WHEN experience_years <= 5 THEN '4-5 years'
        WHEN experience_years <= 8 THEN '6-8 years'
        WHEN experience_years <= 10 THEN '9-10 years'
        WHEN experience_years <= 15 THEN '11-15 years'
        ELSE '15+ years'
    END as experience_bucket,
    COUNT(*) as count,
    ROUND(COUNT(*)::numeric * 100 / SUM(COUNT(*)) OVER (), 2) as pct_of_network
FROM candidates 
WHERE status = 'network_accepted' 
  AND open_to_opportunities = true
  AND deleted_at IS NULL
GROUP BY 1
ORDER BY MIN(experience_years);

6. Geographic Distribution (Top 20)

Country	Count	% of Network
BR (Brazil)	2,795	49.02%
AR (Argentina)	645	11.31%
MX (Mexico)	552	9.68%
CO (Colombia)	441	7.73%
UY (Uruguay)	147	2.58%
Unknown	130	2.28%
PE (Peru)	104	1.82%
CR (Costa Rica)	95	1.67%
CL (Chile)	89	1.56%
DO (Dominican Republic)	85	1.49%
US (United States)	69	1.21%
BO (Bolivia)	68	1.19%
EC (Ecuador)	61	1.07%
ES (Spain)	51	0.89%
GT (Guatemala)	33	0.58%
PY (Paraguay)	31	0.54%
CA (Canada)	31	0.54%
PT (Portugal)	28	0.49%
VE (Venezuela)	26	0.46%
SV (El Salvador)	25	0.44%

Key Insight: Nearly half (49%) of the network is from Brazil. Top 4 countries (BR, AR, MX, CO) represent 78% of the network.

-- Query: Geographic distribution
SELECT 
    COALESCE(ts_interview->>'country', 'Unknown') as country,
    COUNT(*) as count,
    ROUND(COUNT(*)::numeric * 100 / SUM(COUNT(*)) OVER (), 2) as pct_of_network
FROM candidates 
WHERE status = 'network_accepted' 
  AND open_to_opportunities = true
  AND deleted_at IS NULL
GROUP BY ts_interview->>'country'
ORDER BY count DESC
LIMIT 20;

7. Last Touchpoint Analysis

A touchpoint is defined as communication FROM the candidate:

Email: Email received FROM the candidate (via messages table where from_id = candidate.user_id)
WhatsApp: Message received FROM the candidate (direction = 'received')
Recording: Interview/meeting recording associated with the candidate

Time Since Last Touchpoint

Bucket	Count	% of Network
Last 30 days	568	9.96%
31-90 days	414	7.26%
91-180 days	612	10.73%
181-365 days	1,031	18.07%
Over 1 year	2,781	48.75%
No touchpoint	299	5.24%

Key Insight: 54% of candidates (3,080) have not communicated in over a year or have no touchpoint record. Only 10% have had communication in the last 30 days.

-- Query: Last touchpoint analysis (emails FROM candidates)
WITH candidate_emails AS (
    SELECT c.id as candidate_id, MAX(m.date) as last_email_date
    FROM candidates c
    JOIN messages m ON m.candidate_id = c.id AND m.from_id = c.user_id AND m.kind = 'email'
    WHERE c.status = 'network_accepted' 
      AND c.open_to_opportunities = true
      AND c.deleted_at IS NULL AND m.deleted_at IS NULL
    GROUP BY c.id
),
candidate_whatsapp AS (
    SELECT t.candidate_id, MAX(m.created_at) as last_whatsapp_date
    FROM im_threads t
    JOIN im_messages m ON m.thread_id = t.id
    JOIN candidates c ON c.id = t.candidate_id
    WHERE c.status = 'network_accepted' 
      AND c.open_to_opportunities = true
      AND c.deleted_at IS NULL AND m.deleted_at IS NULL
      AND m.direction = 'received'
    GROUP BY t.candidate_id
),
candidate_recordings AS (
    SELECT r.candidate_id, MAX(r.started_at) as last_recording_date
    FROM recordings r
    JOIN candidates c ON c.id = r.candidate_id
    WHERE c.status = 'network_accepted' 
      AND c.open_to_opportunities = true
      AND c.deleted_at IS NULL AND r.deleted_at IS NULL
    GROUP BY r.candidate_id
),
all_touchpoints AS (
    SELECT 
        c.id as candidate_id,
        GREATEST(
            COALESCE(ce.last_email_date, '1970-01-01'::timestamp),
            COALESCE(cw.last_whatsapp_date, '1970-01-01'::timestamp),
            COALESCE(cr.last_recording_date, '1970-01-01'::timestamp)
        ) as last_touchpoint
    FROM candidates c
    LEFT JOIN candidate_emails ce ON ce.candidate_id = c.id
    LEFT JOIN candidate_whatsapp cw ON cw.candidate_id = c.id
    LEFT JOIN candidate_recordings cr ON cr.candidate_id = c.id
    WHERE c.status = 'network_accepted' 
      AND c.open_to_opportunities = true
      AND c.deleted_at IS NULL
),
bucketed AS (
    SELECT 
        CASE 
            WHEN last_touchpoint = '1970-01-01'::timestamp THEN 'No touchpoint'
            WHEN last_touchpoint >= NOW() - INTERVAL '30 days' THEN 'Last 30 days'
            WHEN last_touchpoint >= NOW() - INTERVAL '90 days' THEN '31-90 days'
            WHEN last_touchpoint >= NOW() - INTERVAL '180 days' THEN '91-180 days'
            WHEN last_touchpoint >= NOW() - INTERVAL '365 days' THEN '181-365 days'
            ELSE 'Over 1 year'
        END as touchpoint_bucket
    FROM all_touchpoints
)
SELECT touchpoint_bucket, COUNT(*) as count,
    ROUND(COUNT(*)::numeric * 100 / SUM(COUNT(*)) OVER (), 2) as pct_of_network
FROM bucketed
GROUP BY touchpoint_bucket;

Touchpoint Coverage by Type

Touchpoint Type	Candidates with Record	% of Network
Recording	4,104	71.94%
WhatsApp (from candidate)	3,803	66.66%
Email (from candidate)	3,518	61.67%

Most Recent Touchpoint Type

Most Recent Type	Count	% of Network
WhatsApp	3,131	54.88%
Recording	1,203	21.09%
Email	1,072	18.79%
None	299	5.24%

Stale Candidates by Role (Over 1 Year Since Touchpoint)

Role	Stale Count	Total in Role	Stale %
Backend Engineer	1,345	2,214	60.7%
Fullstack Engineer	1,091	2,017	54.1%
Frontend Engineer	526	930	56.6%
Mobile Engineer	308	446	69.1%
DevOps Engineer	134	280	47.9%
Data Engineer	128	295	43.4%
QA Engineer	117	297	39.4%
Engineering Manager	107	197	54.3%
Data/BI Analyst	80	136	58.8%
Solutions Architect	72	130	55.4%
QA Analyst	71	210	33.8%
UX/UI Designer	61	166	36.7%
Data Scientist	51	129	39.5%
Machine Learning	51	136	37.5%
Other	49	71	69.0%

Key Insight: Mobile Engineers (69.1%), "Other" roles (69.0%), and Backend Engineers (60.7%) have the highest staleness rates. QA Analysts (33.8%) and UX/UI Designers (36.7%) are the most engaged.

8. Tech Stack Combinations Analysis

This section analyzes combinations of tech strengths to identify common patterns and gaps.

Top Tech Strength Pairs (Co-occurrence)

Skill 1	Skill 2	Count	% of Network
JavaScript	React	1,262	22.12%
JavaScript	TypeScript	1,140	19.98%
React	TypeScript	1,045	18.32%
Node.js	React	910	15.95%
JavaScript	Node.js	866	15.18%
Node.js	TypeScript	797	13.97%
Python	AWS	754	13.22%
React	AWS	621	10.89%
JavaScript	AWS	599	10.50%
AWS	Docker	580	10.17%
PostgreSQL	AWS	551	9.66%
TypeScript	AWS	541	9.48%
Java	Springboot	457	8.01%
AWS	Kubernetes	452	7.92%
C#	.NET	443	7.77%
Docker	Kubernetes	403	7.06%
React	Next.js	385	6.75%
Java	Spring	371	6.50%
Django	Python	281	4.93%

Key Insight: The React + Node.js + TypeScript stack dominates. Python + AWS is the strongest non-JavaScript pairing.

Frontend Framework Distribution (Frontend/Fullstack Roles)

Framework	Candidates	% of Frontend/Fullstack
React	1,631	67.59%
Angular	466	19.31%
Vue.js	393	16.29%
Next.js	384	15.91%
Angular.js	230	9.53%
Svelte	16	0.66%
Ember	7	0.29%

Key Insight: React dominates at 68%. Vue.js and Svelte are underrepresented compared to market demand.

Backend Language Distribution (Backend/Fullstack Roles)

Language	Candidates	% of Backend/Fullstack
Node.js	1,136	36.56%
Java	858	27.62%
Python	715	23.01%
C#	528	16.99%
.NET	478	15.38%
PHP	356	11.46%
Go (Golang)	229	7.37%
Ruby	153	4.92%
Elixir	61	1.96%
Scala	46	1.48%
Rust	25	0.80%

Key Insight: Go (7.4%), Rust (0.8%), and Elixir (2%) are significantly underrepresented given market demand.

Fullstack Stack Combinations (Frontend + Backend)

Frontend	Backend	Count	% of Fullstack
React	Node.js	778	38.57%
React	Python	276	13.68%
React	Java	224	11.11%
Next.js	Node.js	210	10.41%
Vue.js	Node.js	159	7.88%
Angular	Node.js	158	7.83%
React	C#	158	7.83%
React	.NET	156	7.73%
React	PHP	146	7.24%
Angular	C#/.NET	125	6.20%
Vue.js	PHP	114	5.65%
Angular	Java	103	5.11%
React	Ruby	78	3.87%
React	Go	61	3.02%
Vue.js	Python	63	3.12%
Vue.js	Go	16	0.79%

Underrepresented Fullstack Combos:

Combination	Count	Notes
Vue.js + Go	16	High demand, very rare
Next.js + Go	10	Modern stack, scarce
Svelte + Any	9	Emerging framework, almost none
React + Rust	7	Cutting-edge, extremely rare
Angular + Python	43	Enterprise combo, limited

Backend Engineer Language Breakdown

Language	Count	% of Backend
Java	734	33.15%
Node.js	620	28.00%
Python	545	24.62%
C#	419	18.93%
.NET	384	17.34%
PHP	248	11.20%
Go (Golang)	205	9.26%
Kotlin	161	7.27%
Ruby	129	5.83%
Elixir	50	2.26%
Scala	38	1.72%
Rust	19	0.86%

Mobile Engineer Platform Breakdown

Technology	Count	% of Mobile
Kotlin	181	40.58%
Swift	174	39.01%
Android	165	37.00%
iOS	145	32.51%
Java	142	31.84%
React Native	88	19.73%
SwiftUI	88	19.73%
Objective-C	68	15.25%
Flutter	61	13.68%
Jetpack Compose	16	3.59%

Key Insight: Native development (Kotlin/Swift) dominates. Flutter (13.7%) is underrepresented given its growing popularity.

Cloud + Infrastructure Combinations (DevOps/Backend/Architect Roles)

Cloud	Infrastructure	Count	% of Infra Roles
AWS	Docker	441	17.65%
AWS	Kubernetes	369	14.77%
AWS	Terraform	244	9.76%
AWS	Jenkins	140	5.60%
GCP	Kubernetes	135	5.40%
GCP	Docker	130	5.20%
GCP	Terraform	98	3.92%
Azure	Docker	97	3.88%
Azure	Kubernetes	91	3.64%
AWS	GitHub Actions	84	3.36%

Key Insight: AWS dominates cloud. GCP and Azure combinations are significantly less common.

Data/ML Stack (Data Roles)

Technology	Count	% of Data Roles
Python	493	88.35%
SQL	368	65.95%
Airflow	141	25.27%
Databricks	138	24.73%
PowerBI	134	24.01%
Pandas	131	23.48%
ETL	105	18.82%
Tensorflow	102	18.28%
PySpark	96	17.20%
Snowflake	95	17.03%
PyTorch	84	15.05%
Tableau	84	15.05%
BigQuery	78	13.98%
LLMs	73	13.08%
scikit-learn	71	12.72%
DBT	61	10.93%

Key Insight: Python + SQL is the foundation (88%/66%). Modern data stack (DBT, Snowflake, Databricks) has good representation. LLM expertise (13%) is growing.

Summary of Identified Gaps

Gap Category	Specific Gap	Current Supply
Frontend Frameworks	Vue.js specialists	16.29%
	Svelte	0.66%
Backend Languages	Go (Golang)	7-9%
	Rust	<1%
	Elixir	~2%
Fullstack Combos	Vue.js + Go	16 people
	Vue.js + Python	63 people
	Next.js + Go	10 people
	Svelte + anything	9 people
Mobile	Flutter	13.68%
Cloud	GCP expertise	~5-7%
	Azure expertise	~4%

9. 2025 Job Demand vs Candidate Supply Gap Analysis

This section analyzes 677 job openings that were open at some point during 2025 and compares their requirements against the current candidate supply.

2025 Job Overview

Total jobs open during 2025: 677
Status: 637 closed, 34 published, 6 draft

Role Demand vs Supply

Role	Jobs 2025	Candidates	Candidates/Job	Gap Assessment
Fullstack Engineer	208 (30.7%)	2,017	9.7	⚠️ Moderate
Backend Engineer	186 (27.5%)	2,214	11.9	✅ Good
Frontend Engineer	113 (16.7%)	930	8.2	⚠️ Moderate
QA Engineer	46 (6.8%)	297	6.5	⚠️ Tight
Mobile Engineer	38 (5.6%)	446	11.7	✅ Good
DevOps Engineer	37 (5.5%)	280	7.6	⚠️ Moderate
Data Engineer	28 (4.1%)	295	10.5	✅ Good
Machine Learning	17 (2.5%)	136	8.0	⚠️ Moderate
QA Analyst	13 (1.9%)	210	16.2	✅ Good
Data Scientist	12 (1.8%)	129	10.8	✅ Good
UX/UI Designer	12 (1.8%)	166	13.8	✅ Good
Product Designer	9 (1.3%)	106	11.8	✅ Good
Engineering Manager	8 (1.2%)	197	24.6	✅ Good
Solutions Architect	7 (1.0%)	130	18.6	✅ Good
Product Manager	6 (0.9%)	81	13.5	✅ Good

Seniority Demand vs Supply

Seniority	Jobs 2025	% Demand	Candidates	% Supply	Candidates/Job	Gap
IC5	272	40.2%	1,590	27.9%	5.8	⚠️ Tight
IC4	233	34.4%	3,221	56.5%	13.8	✅ Surplus
IC6	107	15.8%	133	2.3%	1.2	🔴 Critical
Lead	64	9.5%	8	0.1%	0.1	🔴 Critical
IC3	46	6.8%	480	8.4%	10.4	✅ Good
Manager	33	4.9%	82	1.4%	2.5	⚠️ Tight
IC2	10	1.5%	45	0.8%	4.5	⚠️ Moderate

Key Insight: IC6 and Lead seniorities are critically undersupplied. Demand heavily favors IC5 (40%) but supply is concentrated at IC4 (57%).

Skill/Stack Demand vs Supply

Skill	Jobs 2025	Candidates	Candidates/Job	Gap
React	187 (36%)	1,814	9.7	⚠️ Moderate
Python	162 (31%)	1,495	9.2	⚠️ Moderate
Node.js	98 (19%)	1,291	13.2	✅ Good
TypeScript	95 (18%)	1,498	15.8	✅ Good
JavaScript	71 (14%)	1,960	27.6	✅ Surplus
AWS	40 (8%)	1,829	45.7	✅ Surplus
Go (Golang)	30 (6%)	279	9.3	⚠️ Moderate
Java	29 (6%)	1,256	43.3	✅ Surplus
Vue.js	28 (5%)	413	14.8	✅ Good
Next.js	24 (5%)	404	16.8	✅ Good
LLMs	21 (4%)	111	5.3	🔴 Tight
Django	22 (4%)	293	13.3	✅ Good
PostgreSQL	16 (3%)	891	55.7	✅ Surplus
OpenAI	12 (2%)	52	4.3	🔴 Critical
LangChain	11 (2%)	56	5.1	🔴 Tight
GenAI	11 (2%)	23	2.1	🔴 Critical
Flutter	11 (2%)	85	7.7	⚠️ Tight
Playwright	14 (3%)	134	9.6	⚠️ Moderate
RAG	7 (1%)	34	4.9	🔴 Critical

Critical Gaps: Role + Seniority Combinations

These combinations have < 2 candidates per job - critical supply shortage:

Role	Seniority	Jobs 2025	Candidates	Gap
Fullstack Engineer	IC6	48	33	🔴 0.7/job
Backend Engineer	IC6	41	57	🔴 1.4/job
Backend Engineer	Lead	23	1	🔴 0.0/job
Fullstack Engineer	Lead	22	0	🔴 0.0/job
Frontend Engineer	IC6	17	8	🔴 0.5/job
Machine Learning	IC5	17	33	🔴 1.9/job
Backend Engineer	Manager	14	7	🔴 0.5/job
Machine Learning	IC6	13	10	🔴 0.8/job
Fullstack Engineer	Manager	12	6	🔴 0.5/job
QA Engineer	Lead	10	0	🔴 0.0/job
QA Engineer	IC6	9	2	🔴 0.2/job
DevOps Engineer	IC6	9	11	🔴 1.2/job
Frontend Engineer	Lead	8	0	🔴 0.0/job
Machine Learning	Lead	7	0	🔴 0.0/job
Data Engineer	IC6	6	4	🔴 0.7/job
Data Scientist	IC6	6	4	🔴 0.7/job

AI/ML Skills Gap (High-Growth Market)

Skill	Jobs 2025	Candidates	Candidates/Job	Assessment
LLMs	21	111	5.3	🔴 Tight
OpenAI	12	52	4.3	🔴 Critical
LangChain	11	56	5.1	🔴 Tight
GenAI	11	23	2.1	🔴 Critical
RAG	7	34	4.9	🔴 Critical
Anthropic	3	4	1.3	🔴 Critical
PyTorch	3	93	31.0	✅ Surplus
Tensorflow	1	109	109.0	✅ Surplus
scikit-learn	2	78	39.0	✅ Surplus

Key Insight: Traditional ML skills (PyTorch, Tensorflow, scikit-learn) have good supply. Generative AI skills (LLMs, OpenAI, LangChain, GenAI, RAG) are critically undersupplied with only 2-5 candidates per job.

Summary: Top Priority Gaps to Address

Priority	Gap Type	Specific Gap	Severity
1	Seniority	IC6 (Principal) - only 133 candidates for 107 jobs	🔴 Critical
2	Seniority	Lead - only 8 candidates for 64 jobs	🔴 Critical
3	Seniority	Manager - only 82 candidates for 33 jobs	⚠️ Tight
4	AI Skills	GenAI - 23 candidates for 11 jobs	🔴 Critical
5	AI Skills	OpenAI - 52 candidates for 12 jobs	🔴 Critical
6	AI Skills	LangChain - 56 candidates for 11 jobs	🔴 Tight
7	AI Skills	RAG - 34 candidates for 7 jobs	🔴 Critical
8	Role+Level	Fullstack IC6 - 33 for 48 jobs	🔴 Critical
9	Role+Level	Backend Lead - 1 for 23 jobs	🔴 Critical
10	Role+Level	ML IC5/IC6 - 43 for 30 jobs	🔴 Critical

10. Salary Analysis: Budget vs Expectations

This section analyzes candidate salary expectations against job budgets to identify where salary mismatches reduce effective supply.

Candidate Salary Expectations by Seniority

Seniority	Candidates	Avg Salary	Median	P25	P75
Director	7	$183,686	$180,000	$132,500	$230,000
IC6	133	$104,245	$100,000	$80,000	$120,000
Manager	81	$99,243	$96,000	$72,000	$120,000
Lead	8	$96,375	$77,500	$57,000	$117,000
IC5	1,587	$87,480	$84,000	$70,000	$100,000
IC4	3,215	$72,778	$72,000	$60,000	$84,000
IC3	478	$51,693	$48,000	$36,000	$60,000
IC2	45	$33,682	$30,000	$24,000	$38,400

Job Budget by Seniority (2025 Jobs)

Seniority	Jobs	Avg Min	Avg Max	Median Max
Director	4	$97,500	$127,500	$117,500
IC6	107	$68,950	$99,411	$100,000
Manager	33	$70,242	$99,697	$100,000
Lead	64	$67,234	$96,406	$100,000
IC5	272	$62,233	$87,829	$89,200
IC4	233	$59,471	$86,262	$80,000
IC3	46	$38,620	$55,348	$57,000
IC2	10	$23,650	$36,100	$32,500

Salary Mismatch by Seniority

Seniority	Candidates	Median Job Budget	Median Ask	Within Budget	% Affordable	% Priced Out
IC4	3,215	$80,000	$72,000	2,250	70.0%	17.5%
IC5	1,587	$89,200	$84,000	926	58.3%	20.9%
IC3	478	$57,000	$48,000	303	63.4%	18.2%
IC6	133	$100,000	$100,000	76	57.1%	19.5%
Manager	81	$100,000	$96,000	48	59.3%	22.2%
IC2	45	$32,500	$30,000	27	60.0%	28.9%
Director	7	$117,500	$180,000	1	14.3%	71.4%

Key Insight: Directors are almost entirely priced out (71%). IC5 and Manager levels have ~20% priced out, reducing effective supply.

Salary Mismatch by Role

Role	Candidates	Job Budget (Median)	Candidate Ask (Median)	Gap	% Affordable
Backend Engineer	2,201	$96,000	$75,000	-$21,000	80.2% ✅
Fullstack Engineer	2,000	$90,000	$74,400	-$15,600	74.1%
Frontend Engineer	922	$90,000	$72,000	-$18,000	76.5%
Mobile Engineer	443	$90,000	$76,000	-$14,000	72.9%
DevOps Engineer	276	$100,000	$80,000	-$20,000	79.0%
Data Engineer	292	$95,500	$72,000	-$23,500	79.8% ✅
Machine Learning	135	$100,000	$84,000	-$16,000	81.5% ✅
Engineering Manager	196	$100,000	$100,000	$0	53.1% ⚠️
Solutions Architect	129	$100,000	$96,000	-$4,000	66.7%
Product Designer	105	$60,000	$65,000	+$5,000	47.6% 🔴
QA Engineer	295	$70,000	$60,000	-$10,000	64.4%
QA Analyst	209	$55,000	$48,000	-$7,000	67.5%

Key Insight: Product Designers are the only role where candidates ask MORE than typical budgets (+$5k gap, only 48% affordable).

Effective Supply: Raw vs Affordable (Critical Combinations)

When accounting for salary, the effective supply drops significantly:

Role	Seniority	Jobs	Budget (P75)	Raw Supply	Affordable	Raw/Job	Affordable/Job
Backend Engineer	Lead	23	$120,000	1	1	0.0	🔴 0.0
Frontend Engineer	IC6	17	$120,000	8	5	0.5	🔴 0.3
Backend Engineer	Manager	14	$120,000	7	5	0.5	🔴 0.4
Fullstack Engineer	Manager	12	$120,000	6	5	0.5	🔴 0.4
Fullstack Engineer	IC6	48	$120,000	33	22	0.7	🔴 0.5
Machine Learning	IC6	13	$120,000	10	9	0.8	🔴 0.7
Backend Engineer	IC6	41	$120,000	57	46	1.4	🔴 1.1
Machine Learning	IC5	17	$120,000	33	29	1.9	⚠️ 1.7
QA Engineer	IC5	24	$82,750	81	66	3.4	⚠️ 2.8
Data Engineer	IC5	15	$100,000	65	52	4.3	⚠️ 3.5
Frontend Engineer	IC5	44	$100,000	228	165	5.2	⚠️ 3.8
Fullstack Engineer	IC5	111	$100,000	589	444	5.3	⚠️ 4.0

Salary-adjusted reality:

Fullstack IC6: 33 total → only 22 affordable (0.5/job)
Backend IC5: 704 total → only 577 affordable (6.6/job)
Frontend IC5: 228 total → only 165 affordable (3.8/job)

Candidates "Priced Out" (Asking Above P75 Budget)

Role + Seniority	Total	Affordable	Priced Out	% Lost
Fullstack Engineer IC4	1,187	936	251	21.1%
Backend Engineer IC4	1,267	1,017	250	19.7%
Fullstack Engineer IC5	589	444	145	24.6%
Backend Engineer IC5	704	577	127	18.0%
Frontend Engineer IC4	582	477	105	18.0%
Mobile Engineer IC4	277	198	79	28.5%
Frontend Engineer IC5	228	165	63	27.6%
Mobile Engineer IC5	121	78	43	35.5%

Key Insight: Mobile Engineers at IC5 level have the highest "priced out" rate (35.5%), followed by Frontend IC5 (27.6%).

Salary Distribution by Seniority

Seniority	<$50k	$50-70k	$70-90k	$90-110k	$110-130k	$130k+
IC4	473	991	1,051	481	137	82
IC5	80	277	569	398	148	115
IC3	248	143	66	14	7	0
IC6	3	10	35	36	23	26
Manager	6	11	20	14	13	17
IC2	40	3	2	0	0	0

Summary: Salary-Adjusted Gap Analysis

Priority	Role + Seniority	Jobs	Raw Supply	Affordable	Affordable/Job	Issue
1	Backend Lead	23	1	1	🔴 0.0	No candidates at any price
2	Fullstack Lead	22	0	0	🔴 0.0	No candidates at any price
3	Frontend IC6	17	8	5	🔴 0.3	Salary + supply gap
4	Backend Manager	14	7	5	🔴 0.4	Salary + supply gap
5	Fullstack Manager	12	6	5	🔴 0.4	Salary + supply gap
6	Fullstack IC6	48	33	22	🔴 0.5	Salary reduces supply 33%
7	ML IC6	13	10	9	🔴 0.7	Tight even at budget
8	Backend IC6	41	57	46	🔴 1.1	Salary reduces supply 19%
9	ML IC5	17	33	29	⚠️ 1.7	Tight
10	Fullstack IC5	111	589	444	⚠️ 4.0	25% priced out

11. Data Quality Notes

Candidates without roles: 20 candidates have no roles assigned
Unknown seniority: 137 candidates (2.4%) have no seniority data

-- Query: Candidates without roles
SELECT COUNT(*) as no_roles_count
FROM candidates 
WHERE status = 'network_accepted' 
  AND open_to_opportunities = true
  AND deleted_at IS NULL
  AND (roles IS NULL OR array_length(roles, 1) IS NULL);

Appendix: All Tech Strengths

The network includes candidates with 1,469 distinct tech strengths. The full list is available in the database query results.

Report generated from platform database. All queries filter for status = 'network_accepted' AND open_to_opportunities = true AND deleted_at IS NULL.

aseba/network-analysis-report.md