12 weeks · intensive
For whoever has a deadline
About 8 hours a week, in 1 cycle focused on the gaps that weigh most.
Career in 2026 and 2027
Data Solutions Architect
A Data Solutions Architect designs how data is modeled, moved, stored and governed so that analytics and machine learning can rely on it. The role grows from building sound warehouse models and pipelines to setting platform-wide contracts, streaming and ML data infrastructure.
Content reviewed on October 20, 2026
- Levels
- 4
- Skills mapped at Mid-level
- 8
What each level must prove
Mid-level
Is accountable for the design of a data domain end to end: storage layer, warehouse, and the metadata that makes it traceable.
You now choose between a lakehouse and a warehouse layout for a domain and own its lineage and catalog, not just the tables. The change from junior is that your designs are adopted by other teams rather than only reviewed.
Average score required: 72%
Dimensional Modeling
Why: Lets you model slowly changing dimensions and shared conformed dimensions across several fact tables, so two teams report the same customer count.
Normalization Patterns
Why: Lets you justify a denormalized serving table with measured query cost, instead of denormalizing by habit and inheriting inconsistent copies.
ETL/ELT Patterns
Why: Lets you pick ELT on a cloud warehouse for a new domain and design idempotent loads, so a rerun after an outage does not duplicate rows.
Orchestration Tools
Why: Lets you structure cross-team dependencies and backfills in the orchestrator so a late upstream dataset does not trigger a chain of manual reruns.
Lakehouse Architecture
Why: Lets you lay out bronze, silver and gold layers on an open table format, so raw files become reliable, queryable tables with ACID guarantees.
Warehouse Design
Why: Lets you size warehouses, separate workloads and tune a slow query, so a heavy month-end job stops starving the executive dashboards.
Data Lineage
Why: Lets you trace a wrong dashboard number back to the source column and list which reports a schema change would break before it ships.
Data Cataloging
Why: Lets you set up a catalog and business glossary so an analyst finds the certified revenue table instead of building a third unofficial copy.
Study for Data Solutions Architect: trails and practice exams
Each level of this career maps to a study trail. Read the full topic map for free, then measure your level.
Two ways to move up one level
40 weeks · regular
Alongside work
About 4 hours a week, in 3 cycles. Each cycle carries what was left from the previous one.
Frequently asked questions
What is Data Solutions Architect?
A Data Solutions Architect designs how data is modeled, moved, stored and governed so that analytics and machine learning can rely on it. The role grows from building sound warehouse models and pipelines to setting platform-wide contracts, streaming and ML data infrastructure.
What must each level of Data Solutions Architect prove?
Data Solutions Architect is mapped in 4 levels: Junior, Mid-level, Senior, and Expert. Open each level above to see the skills it must prove.
How long does the Data Solutions Architect plan take?
The plan has 2 paces: 12 weeks per level at about 8 hours a week (intensive) and 40 weeks per level at about 4 hours a week (regular).
How the plan works
AI tutor
A study session on each new topic, at your level.
AI school
Hands-on lessons on using AI in your career's daily work.
Assessments
Each requirement is proven by measurement, not by a checkbox.
Stage review
At the end of each cycle you see what moved and what carries over.
Find out which level you are at
A short diagnostic measures the skills you already have. The journey starts from there.