Modern Data Architecture
Designing Systems That Grow With You
Data architecture is the blueprint that determines how your systems store, move, and expose data across the business. I design cloud-native architectures on AWS and Azure including Databricks-based lakehouses that are modular, cost-efficient, and built to accommodate growth without rewrites. A good architecture is invisible it just works.
Outdated data infrastructure slows teams down. I help companies modernize their stack with clear, scalable architecture that enables fast insights, future flexibility, and simplified compliance.
What You Get
- Architecture Decision Record (ADR) with 3 options and chosen path
- Terraform modules for all provisioned infrastructure (dev, staging, prod)
- Data domain map and ownership matrix
- Delta Lake / Iceberg table design and partitioning strategy
- Cost dashboard and tagging taxonomy
- 60-minute team onboarding session
Solving Real Data Architecture Challenges
Architecture that can't keep up with the business
A schema decided in year one shouldn't require a full rewrite in year three. I design modular, domain-separated lakehouses where adding a new source or data product is a configuration change, not an architecture change.
Cloud bill growing faster than the data team
Unmanaged Spark clusters and oversized warehouses burn money 24/7. I design auto-scaling compute, lifecycle-managed storage tiers, and Delta OPTIMIZE jobs that typically cut cloud spend 20–40%.
Compliance and governance as an afterthought
GDPR, ISO 27001, and SOC2 are easier to bake in at design time than retrofit later. I build column-level encryption, row-level security, and audit logging into the architecture from day one.
Pillars of My Data Architecture Approach
Modular by domain, not by layer
Data mesh-inspired boundaries mean the marketing domain owns its data products end-to-end, and changes there don't cascade into finance pipelines.
Cost-observable infrastructure
Every compute resource is tagged, every storage bucket has a lifecycle policy. I instrument cost dashboards at architecture time, not after the first invoice shock.
Reproducible via IaC
Terraform modules for every environment so dev, staging, and prod are identical. New engineers can spin up a local copy in under an hour.
My Approach
Existing stack audit (weeks 1–2)
I review current architecture diagrams (or create them), cloud spend reports, and team pain points. Output: a prioritized opportunity map.
Architecture blueprint (weeks 2–3)
I produce an Architecture Decision Record (ADR) with three options and a recommended path. You approve before implementation starts.
Incremental migration (weeks 3–7)
I migrate domain by domain so your existing pipelines keep running while the new layer goes live in parallel.
Terraform handover (final week)
All infrastructure is codified in a Terraform repo your team owns. I deliver a 60-minute onboarding session.
Glossary
- Data lakehouse
- An architecture that combines the low-cost storage of a data lake with the query performance and ACID guarantees of a data warehouse, typically implemented on Delta Lake or Apache Iceberg.
- Data mesh
- A decentralised data architecture paradigm where individual domain teams own, publish, and maintain their own data products rather than funnelling everything through a central team.
- Infrastructure-as-code (IaC)
- The practice of managing cloud infrastructure through machine-readable configuration files (e.g. Terraform, Pulumi) rather than manual console clicks enabling versioning, review, and repeatable deploys.
- Lambda architecture
- A data processing pattern that runs a slow batch layer and a fast real-time layer in parallel, merging results at query time. Often replaced today by streaming-first designs.
- Medallion architecture
- A layered data design pattern (Bronze → Silver → Gold) that progressively cleans and enriches raw data into business-ready tables applicable to both lake and lakehouse setups.
Common Questions
What is a data lakehouse and do I need one?
A data lakehouse combines data lake storage (cheap, schema-flexible) with warehouse-grade query performance and ACID transactions, typically via Delta Lake or Apache Iceberg on Databricks. You likely need one if you're running both analytical queries and ML workloads, or if your current lake has no data quality guarantees.
How much does a data architecture engagement cost?
I work on a project basis and provide a fixed-price estimate after an initial scoping call. You get a complete Architecture Decision Record, Terraform modules, and a documented migration plan typically delivered over 4–8 weeks.
Can you migrate our on-premise data warehouse to the cloud?
Yes, cloud data warehouse migrations are one of the most common engagements. I handle schema translation, historical data migration, pipeline re-platforming, and parallel-run validation before the legacy system is decommissioned.
Ready to Build Better Data Systems?
Let's discuss how I can help you modernize your data infrastructure and unlock the full potential of your data.
Schedule a Free Consultation