Skills Gap & Competency Alignment

NovaTech Engineering · 890 Engineers · Q4 2024

Coverage Ratio
68%
-3% from target
Critical Gaps
23
High priority
Role Readiness
74%
+2% QoQ
Upskilling Velocity
+0.4
levels/qtr
Assessment Freshness
42
days avg

Skills Coverage Heatmap — Assessed vs Required Proficiency

Role Family
Kubernetes
Terraform
AWS
Python
Spark
React
Security
Docker
SQL
ML Ops
CI/CD
Kafka
Cloud Architecture
52%
58%
78%
72%
64%
58%
71%
82%
76%
59%
73%
66%
Data Engineering
65%
63%
74%
84%
79%
57%
67%
72%
88%
61%
68%
77%
Full Stack Dev
66%
54%
69%
86%
62%
91%
73%
78%
81%
51%
74%
64%
DevOps
76%
71%
83%
77%
68%
65%
79%
89%
72%
63%
85%
74%
Security Eng
61%
59%
73%
76%
67%
62%
92%
81%
78%
58%
77%
69%
ML Engineering
64%
60%
72%
87%
82%
66%
71%
74%
79%
84%
68%
73%
≥85%
70-84%
60-69%
<60%

Priority Skills Gap Radar

Demand vs Supply Delta

Role Readiness Detail — Top 10 Roles

Role Headcount Readiness Critical Gaps Avg Freshness Status
Senior Cloud Architect 47 52% K8s, Terraform 38d Critical
Staff Data Engineer 62 74% Kafka, MLOps 41d Monitor
Principal Full Stack 38 79% MLOps 35d On Track
Senior DevOps Engineer 55 82% — 29d On Track
Security Engineer II 42 71% K8s, Terraform 44d Monitor
ML Engineer II 34 76% Terraform 37d On Track
Staff Cloud Architect 28 58% K8s, Terraform 42d Critical
Senior Data Engineer 71 77% Kafka 39d On Track
Principal DevOps Eng 19 85% — 26d On Track
Senior Security Engineer 31 73% K8s 47d Monitor

Upskilling Velocity Trend

Cross-Functional Skill Overlap

47%
Cloud ↔ DevOps
38%
Data ↔ ML
29%
Full Stack ↔ Security
34%
DevOps ↔ Security
26%
Cloud ↔ ML
31%
Data ↔ Full Stack
% engineers with proficiency in both domains

Assessment Freshness

42
days avg
Target: <30 days

Cert Alignment Score

75%
Cert ↔ Competency match

Reskilling $/Gap

$2.4k
per FTE per gap closed

Critical Skill Alerts

Kubernetes proficiency 33% below target in Cloud Architecture
47 engineers affected · Avg gap: 2.1 levels · Est. training: 6-8 weeks
P0
Terraform expertise lagging across Cloud & Security roles
73 engineers affected · Avg gap: 1.8 levels · Est. training: 4-6 weeks
P0
MLOps skills deficit in ML Engineering and Data teams
58 engineers affected · Avg gap: 1.4 levels · Est. training: 3-5 weeks
P1
Kafka proficiency gap emerging in Data Engineering
41 engineers affected · Avg gap: 1.2 levels · Est. training: 2-4 weeks
P1

Manager vs Self-Assessed Gap

Positive variance = self-assessment higher than manager validation
Skills Taxonomy: Workday Skills Cloud v3.2 · Last Refresh: Nov 2024
Last Assessment Cycle: Q4 2024 (Oct 15 - Nov 30)
Next Review: Feb 2025