Data architecture + target-state design
Blueprint for how sources connect, where data lives, security boundaries, and how teams consume governed KPIs.
What it includes
Data modeling (operational + analytical)
Canonical models (entities, hierarchies, facts) built to support reporting, forecasting, and AI safely.
Ingestion + pipeline engineering
Incremental loads, orchestration, error handling, and patterns for APIs, files, and databases.
Data quality + monitoring + lineage-ready documentation
Freshness/completeness checks, anomaly detection, runbooks, and documentation that supports audits and onboarding.
Performance + cost optimization
Partitioning/clustering, query tuning, capacity planning (Fabric), and FinOps guardrails.
Deliverables
Target-state architecture + implementation plan
Curated data models and transformation logic
Production-ready pipelines with monitoring and alerts
Documentation pack (lineage-ready, runbooks, KPI definitions)
FAQ
What is the difference between data architecture and data engineering?
Architecture defines the blueprint (models, patterns, governance, system contracts). Engineering implements it (pipelines, transformations, orchestration, and operations).
Do we need to rebuild our platform to improve reporting?
Not always. We stabilize and model what you have first, then modernize selectively where reliability, governance, or cost requires it.
Do you replace our stack?
Only when necessary; we integrate and harden first, then recommend changes based on measurable benefits.
How do you keep it maintainable?
Standard patterns, monitoring, documentation, and clear ownership—so teams can operate without heroics.