Technical Content Writer
Job description
About the role
Specialized writers convert complex modernization work into narratives that move pilots to production. Enterprise stakeholders rely on these documents to understand risk, scope, and value. This role demands deep technical judgment instead of generic marketing language.
Data roles turn raw information into decisions. Analysts query databases and build dashboards. Data scientists build models that predict outcomes. Data engineers build the pipelines that move and store data. All three work closely with business teams and need a mix of statistics, coding, and communication. Nearly every modern company runs on data teams, from startups to banks. A strong portfolio of past analyses matters more than degrees in many hiring decisions.
Key facts
What you'll do
Production systems running legacy migrations and cloud transformations are translated into clear documentation for US enterprise audiences. Architecture diagrams, validated workflows, and implementation steps help data engineers and leaders adopt and scale these systems with confidence.
Each piece names pain before capability, pairs metrics with baselines, and delivers one contrarian insight that a sharp reader values and bookmarks for future decisions.
US B2B demand generation grows as content ranks in search, resonates with technical readers, and guides data engineers, IT directors, and CFOs through cloud ROI decisions without requiring rewrites for each audience.
Requirements
Content must demonstrate L300/L400 technical depth for complex cloud and data engineering topics such as lakehouse patterns, database migrations, and optimization methods. US B2B buyer expectations demand engineering-led positioning where pain is named before capability and claims stand on measurable baselines and outcomes.
Practical notes
The role centers on cloud and data engineering subject matter, including lakehouse patterns, database migrations, optimization methods, and automated data operations. Diagrams, clear structure, and engineering-level clarity replace jargon to make authoritative content for an enterprise audience. Typical interview steps
Data interviews commonly include a SQL or coding exercise, a statistics question, and a case study. Candidates may be asked to design a metric, interpret an experiment, or build a small model. Some companies give a take-home analysis. Expect questions about past projects and the business impact of your work. Interviewers often evaluate how you communicate uncertainty and business impact, not only the math. Bringing a clean write-up of a past analysis to the interview is well received.
Good to know
Technical content in this role relies on diagrams, clear structure, and engineering-level clarity to communicate complex ideas. Writing for B2B enterprise audiences demands baselines, specific outcomes, and claims that stand out in crowded markets. The position uses concepts and frameworks from cloud and data engineering domains to produce authoritative, actionable documentation that supports pipeline and adoption.
Questions to ask
Worth asking in any interview: how the team measures success, who the role works with daily, what the onboarding looks like, and what the company is trying to achieve this year. Asking what past hires did well is a strong final question. Keep the list short and pick the questions that matter most to you.
Career growth
Data careers grow toward senior analyst, staff data scientist, or data engineering lead. Many professionals specialize in machine learning, analytics, or infrastructure. Cross-functional work with product and engineering teams becomes more important at senior levels. The field changes quickly, so continuous learning is part of the job. Professionals who can translate numbers into decisions tend to advance fastest.
About the company
Mactores is the agent-native AWS modernization firm. We ship AWS modernization to production in weeks, data platforms migrated, legacy applications and databases refactored, AI agents running against real data, for mid-market and lower-enterprise companies in financial services, healthcare and life sciences, internet and software, manufacturing, and TMEGS.