How to Write a Data Architect Job Description

Artificial intelligence has changed the way hiring managers and candidates approach job descriptions. Anyone can now generate a standard description in seconds using AI tools, but those results are often generic and don’t address compliance, branding, or the skills most in demand today.

A Data Architect job description in 2025 needs to do more. It must capture how candidates search, highlight modern responsibilities, and stand out in a market where AI-written postings are common. This guide explains how to write a Data Architect job description that is accurate, attractive, and aligned with current hiring trends.

Why a strong job description matters

• AI alone is not enough: An AI-generated job description may sound polished, but it rarely reflects your organisation’s culture or market positioning.
• Candidates search differently: Job seekers now use skills-based search, asking for roles with “AWS,” “real-time data streaming,” or “AI platform” in the description. If those terms are missing, your posting may not appear.
• Retention starts here: A clear job description sets expectations and reduces turnover. Salary transparency, flexibility, and benefits all influence how long new hires stay.
• Compliance and inclusivity: Language must meet HR and legal standards while encouraging diverse applications.

Step 1: Start with a strong job summary

Your opening sets the tone. It should describe what the role is, why it exists, and how it supports the company’s strategy. Mention AI, cloud, and governance if those are part of the role, because candidates expect them in 2025.

Example:
We are seeking a Data Architect to lead the design and delivery of scalable data platforms. This role will support analytics, AI, and compliance initiatives, while enabling the business to make real-time, data-driven decisions.

Step 2: Define responsibilities clearly

Avoid outdated lists like “manage databases.” Instead, include responsibilities that reflect modern work:

• Design architectures that support AI and machine learning pipelines.
• Build and manage real-time and batch data pipelines (Kafka, Spark, Flink).
• Lead governance, privacy, and compliance (GDPR, HIPAA, CCPA).
• Deliver cloud and hybrid architectures across AWS, Azure, and GCP.
• Collaborate with Data Science, DevOps, and Engineering teams to operationalise models.
• Modernise legacy systems to support new technologies.

Step 3: List required skills and qualifications

Candidates and AI-powered job platforms scan for specific keywords. Break skills into categories:

Technical skills

• Relational and NoSQL database expertise.
• Cloud platform experience (AWS, Azure, GCP).
• Big data frameworks (Spark, Hadoop, Delta Lake).
• Streaming systems (Kafka, Flink, Kinesis).
• Understanding of data governance and security standards.

Soft skills

• Communication and stakeholder management.
• Leadership and team mentoring.
• Strategic thinking and problem-solving.

Step 4: Add preferred skills

Keep required skills realistic. Use this section to highlight advanced or emerging skills:

• Containerisation and orchestration (Docker, Kubernetes).
• Infrastructure as Code (Terraform, CloudFormation).
• Synthetic data, federated learning, or data mesh.
• Industry-specific compliance frameworks.

Step 5: Clarify work environment and benefits

Transparency improves application rates. State:

• Remote, hybrid, or on-site expectations.
• Salary range or bracket.
• Benefits such as training budgets, certifications, and career development.
• Opportunities for progression.

Step 6: Optimise for search

Modern job descriptions should be easy for both people and AI systems to read. To optimise:

• Use long-tail keywords like “senior data architect cloud streaming.”
• Keep sections structured with headers and bullets.
• Expand acronyms once (e.g. “Machine Learning (ML)”).
• Use inclusive language to broaden candidate reach.

Example structure

• Job Title: Data Architect [AI, Cloud, Streaming]
• Job Summary: Role context and impact.
• Responsibilities: 6–8 bullet points.
• Required Skills: Technical and soft skills.
• Preferred Skills: Advanced tools and frameworks.
• Work Environment: Flexibility and conditions.
• Salary and Benefits: Range, perks, development opportunities.