Core Skills of a Semitechnical Professional in 2025

Aug 05, 2025

Understands how software is built, deployed, and maintained — enough to collaborate effectively with engineers.

Basics of frontend vs backend (e.g. client-side rendering, server-side logic): How user interfaces (websites, apps) connect to server-side systems and databases
Understanding the basics of REST APIs (endpoints, auth, rate limits): How different software systems communicate with each other through Application Programming Interfaces or APIs
Basics of application database design (tables, keys, indexes): How data is structured and stored in a production database (SQL or NoSQL) to enable product functionality
Awareness of DevOps concepts (environments, CI/CD, staging vs prod): Familiarity with deployment, version control systems like Git, and maintenance processes that keep software running smoothly

Can build and troubleshoot no-code/low-code systems that move data and trigger actions across tools.

Concepts of event-based triggers and webhooks: How systems can automatically respond to specific events or changes
Understanding data structures and types (objects, arrays, strings, booleans, etc.): How inputs and outputs in a workflow are organized (JSON, arrays, objects) and the data format or type of each data point
Basic JavaScript logic: For formula-based filtering and data manipulation, and creating dynamic outputs (use AI to write/test formulas for the tool you're working in)
Understanding JSON or JavaScript Object Notation: How to read outputs structured as JSON and draft inputs as JSON when calling APIs
Working with APIs using visual tools like Make, Zapier, Airtable, Clay, n8n: HTTP Methods or Verbs (POST, GET, etc), Query Parameters, Headers, Tokens, Authorization methods, rate limits, and of course, reading API docs
Understand RegEx or Regular Expressions: Ability to read and use Regex patterns for data extraction and validation (use AI to write/test RegEx as needed)
Error handling and dead-letter queue (DLQ): Irrespective of the tools in use, understand the root cause of an error via the DLQ, and account for future errors by incorporating error handling in the workflow.

Understands how data is captured, structured, stored, moved, queried, and used across systems.

Basics of first-party data collection: How to gather customer data generated when users interact with a website or an app
Understanding product instrumentation: How to specify exactly what data within an application must be tracked for analysis and activation purposes
Familiarity with data types and how they're stored (e.g. timestamps, booleans, nested objects)
Knowledge of events vs entities and how they’re modeled: Understanding different categories of information and how they relate to business objects and user actions
Understanding application data models (used in production systems) vs analytical models (used in BI tools): Distinguishing between data structured for running applications versus data organized for analysis and reporting
Understanding SQL: Reading and writing simple database queries (often AI-assisted) to retrieve, filter, and analyze data
Understanding how to move data between databases and third-party tools (e.g. from product → warehouse → CRM/tool) for analysis and activation
Familiarity with tools that support this movement: CDPs and ETL tools like Segment, Hightouch, Census, Fivetran, etc.

A modern </Semi> doesn’t need to master any one language or system — they need to:

Learn and practice “Systems Thinking”
Understand how things fit together
Use visual tools and AI wherever possible
Configure systems to talk to each other
Know enough to collaborate deeply with product, data, and engineering
Technical skills applied specifically to solve business problems and improve operational efficiency

This is a working document that covers ~99% of the skills of a <semi>