My Technical Persona
PERSONA
SCORE
＋—
—
＋—
—
＋—
—
All technical personas, descriptions, assumptions and calculations were developed by PAC to demonstrate the overlap and transferability of engineering, data, and security skills.
Identify my Persona
Enter your years in each skill cluster and the approximate % of time dedicated. We compute a weighted % of career and match you to the top Engineering, Data/AI, Architecture, Cloud/Infra, and Security personas best aligned with your experience.
Skill Cluster
Years
% of Time Dedicated
% Time during Career
Standards and Regulations
YEARS
%
0.0%
Standards and management systems (e.g,. ISO 27001, ISO/IEC 42001, ISO 8000). Compliance (e.g., SOC2, HIPAA, PCI-DSS, EU AI Act, GDPR). Audits.
Software Engineering
Front-end Software
YEARS
%
0.0%
Languages and frameworks: JavaScript, CSS/HTML, TypeScript, React, Angular, Vue. Tools: Git, npm/yarn, Vite/Webpack. UI: Consuming RESTful/GraphQL APIs.
Back-end Software & APIs
YEARS
%
0.0%
Languages: Python, Node.js, Go, Java, C#, C++, Ruby. Infrastructure and DevOps: AWS, Azure, Docker, Kubernetes. APIs and systems: RESTful APIs, SQL/NoSQL, and OAuth.
Quality Assurance (QA) & Testing
YEARS
%
0.0%
Test Automation and frameworks: Selenium, Cypress, Playwright, pytest, and JUnit. Test integration into CI/CD pipelines: GitHub Actions, Jenkins, CircleCI.
Data/AI/ML Engineering & Management
Data analytics & Business Intelligence (BI)
YEARS
%
0.0%
Analysis and modeling: Basic to Intermediate SQL, Python/R and Excel. Visualization and reporting: Tableau, Power BI, Looker, Qlik, etc.
Data Science & Engineering
YEARS
%
0.0%
Languages & libraries: Python, R, Advanced SQL. Big data warehousing and processing: Databricks, Snowflake, BigQuery, Redshift. Data pipelines: Airflow, dbt, Kafka, Flink
Applied ML
YEARS
%
0.0%
Languages & frameworks: Python, TensorFlow, XGBoost, PyTorch, scikit-learn. MLOps and deployment: MLflow, Kubeflow, SageMaker, Vertex AI. Modeling: regression, classification, clustering.
Advanced AI Research
YEARS
%
0.0%
Deep learning: neural network architectures (Transformers, CNNs, RNNs), and frameworks (PyTorch, JAX). LLM and genAI: vector databases, RAG pipelines, fine-tuning, prompt-engineering, RLHF. Novel models: architectures and prototypes.
Cloud / Infra / Platform Eng
Cloud & DevOps
YEARS
%
0.0%
Cloud services: AWS, Azure, GCP. Infrastructure: Terraform, Pulumi, CloudFormation. Containerization & orchestration: Docker, Kubernetes, Helm, OpenShift. CI/CD pipelines and serverless functions
Systems & Networks
YEARS
%
0.0%
Operating systems: Linux, Unix, Windows Server. Virtualization: VMware, Hyper-V. Networking: protocals, e.g., TCP/IP, DNS, DHCP, and hardware, e.g., firewalls and load balancers. Routing protocols: BGP, OSPF.
Architecture
YEARS
%
0.0%
Technical: System design, technology selection, integration (via APIs, middleware, or ESBs) and trade-offs between cost vs. technical performance. Enterprise: frameworks (TOGAF, Zachman) and vendor selection.
Security Eng
YEARS
%
0.0%
AppSec: prevent, test and id vulnerabilities (e.g., SAST/DAST). Cloud/Infra security: Cloud Security Posture Management (CSPM), IAM, and firewalls. SecOps/IR: threat detection (SIEM) and response (SOAR)
Total effective years (weighted): 0.0
• Sum of % time across career: 0.0%
Threshold (minimum weighted % for a cluster to count):
(e.g., 0.25 = 25%)