| Detail | For this role |
|---|---|
| Department | Data and Analytics |
| Level | Senior management |
| Reports to | Head of Data |
| Direct reports | Data Engineer, Data Engineering Manager, Data Governance Analyst |
| Experience | 10+ years in data with 3 years in architecture |
Data Architect job description template
Copy this job description, replace the text in square brackets and post it on your careers page or a job portal.
Job title: Data Architect
Department: Data and Analytics
Reports to: Head of Data
Location: [City], [office, branch or site]
About the role
A Data Architect designs how data is stored, modelled, integrated and governed across a company. They set the data platform, models and standards so data is reliable, secure and usable for analytics and applications. The role is senior and cross functional. A good Data Architect builds a scalable, well governed data platform, sets clear models and standards, balances cost and performance, and makes sure analytics and engineering teams can trust and find the data they need.
Key responsibilities
- Design the overall data architecture across warehouses, lakes and pipelines.
- Define data models, schemas and standards for the organization.
- Set the data platform, tools and integration patterns to use.
- Balance performance, scalability, security and cost in the design.
- Set data governance, quality and metadata standards with the team.
- Guide data engineers on pipeline and model design decisions.
- Design for data security, privacy and proper access control.
- Plan data migration and the modernisation of legacy systems.
- Review designs and enforce the architecture standards across teams.
Requirements
- Bachelor's or master's degree in computer science or related field
- Deep experience in data engineering or architecture
- Cloud data platform certification is an advantage
- 10+ years in data with 3 years in architecture
KRAs and KPIs for a Data Architect
Key result areas for the appraisal form, each with a KPI you can measure every month or quarter.
| Key result area | How to measure it |
|---|---|
| Platform reliability | Data platform meets availability and performance targets |
| Model quality | Core data models documented and adopted across teams |
| Governance | Data governance and quality standards applied on new projects |
| Cost and performance | Platform cost and query performance held within targets |
| Security | Data access and privacy controls in place with no breach |
| Adoption | Teams building on the standard platform, not silos |
Skills and tools
Tools used day to day: Snowflake or BigQuery, Apache Spark, SQL and Python, dbt or ETL tools, Data catalog tools.
Reporting line and career path
Next roles: Head of Data, Chief Data Officer, Enterprise Architect
Interview questions for a Data Architect
- How do you design a data platform for both analytics and applications?
- How do you decide between a data warehouse and a data lake?
- How do you model data for a domain with many source systems?
- How do you balance query performance against platform cost?
- How do you build governance and quality into the architecture?
- How do you plan a migration off a legacy data system?
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What does a Data Architect do?
A Data Architect designs how data is stored, modelled, integrated and governed across a company. They set the data platform, models and standards so data is reliable, secure and usable. They guide data engineers, design for security and cost, and plan migrations. Their job is a scalable, trusted data foundation for the business.
What is the difference between a Data Architect and a Data Engineer?
A Data Architect designs the overall data platform, models and standards. A Data Engineer builds and runs the pipelines and datasets within that design. The architect sets the blueprint and standards; the engineer implements and operates them. The architect is usually the more senior, design focused role.
What qualifications does a Data Architect need?
A degree in computer science or a related field, with deep data engineering or architecture experience. Cloud data platform certifications help. Employers value strong data modelling, warehouse and lake design, governance and security knowledge, and the judgement to balance performance and cost at scale.