My Technical Persona

PERSONA

SCORE

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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

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.

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.

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 & 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.

Security

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)

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.

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%)