Designing a secure, scalable data platform isn’t just about modeling pipelines or building dashboards; it starts far earlier. While some of our consultants are focused on business discovery and translating use cases into sustainable data solutions, others, like me, work on a parallel track: ensuring the platform infrastructure is built to support those solutions securely and reliably.

This post offers a different perspective, one rooted in infrastructure and security, on how we turn business priorities into production-ready data architecture. See the companion blog post by Bianca Firtin, “Layer by Layer: Turning Priorities into Data Architecture,” for her perspective.

Translating Use Case into Platform Architecture

When we talk about “architecture,” you may picture data models and transformation logic. But before pipelines can flow or reports can run, there’s a foundational layer that must be in place: the platform.

Part of the discovery process begins not with KPIs or stakeholder interviews, but with networking, identity, cloud infrastructure, compliance, and security teams. We review cloud environments, network diagrams, firewall capabilities, IAM models, and data movement restrictions. We take inventory of data sources, destinations, and patterns, not just for today’s needs, but with an eye toward scaling for future use cases.

Before any data movement begins, the infrastructure to support and secure it must be designed, reviewed, approved, and deployed, a process that often involves cross functional collaboration and careful planning.

Building the Lakehouse Foundation

To support a medallion-style architecture (Bronze → Silver → Gold), several foundational decisions must be made from the platform side.

For organizations using Snowflake as their data Lakehouse, there are multiple ways to implement medallion zoning across accounts, databases, and schemas. Each choice has trade-offs, and we work closely with clients to determine the right design, balancing security, lifecycle management, and operational efficiency.

As we shape the foundation, networking and security come into sharper focus. For example, if the Bronze zone is landing raw data in Amazon S3 or Azure Storage, we must plan for secure and traceable flows into Snowflake. That includes:

• Encryption at rest and in transit (both client and server side)
• Access control via IAM, service principals, or policies
• Use of PrivateLink or Service Endpoints to ensure traffic does not traverse the public internet

These configurations aren’t necessarily difficult to design, but successful implementation requires broad alignment across infrastructure, cloud security, and network engineering teams. There may also be licensing requirements, firewall constraints, or architectural adjustments needed based on geo-distribution, performance expectations, or data residency.

We also account for cloud-to-cloud egress costs and access patterns that may impact scalability or latency, especially when third-party tools like Informatica Cloud are used for ingestion.

Securing the Silver & Gold Layers

Once raw data lands securely in Bronze, many of the platform patterns we establish can be reused and extended in the Silver (Operational Transformation) and Gold (Analytical Curation) layers.

These layers often live in separate storage accounts and Snowflake databases, and as such require:

• Role Based Access Control (RBAC) to govern usage across personas
• Secure credentials storage and rotation practices
• Environment aware access models (e.g., different “consumers” in dev vs. production)
• Design of external integrations to business intelligence tools or downstream services

We treat these deployment and access patterns as modular and repeatable, enabling a smooth path across dev, QA, and production environments while keeping governance intact.atterns as modular and repeatable, enabling a smooth path across dev, QA, and production environments while keeping governance intact.

Embedding Standards and Governance into the Build

Security is not something we “bake in” at the end, it’s a core part of the design.

We prioritize:

• Consistent naming conventions and platform diagrams for clarity and reuse
• Source control not just as a technical system, but as a process integrated with ETL tooling, CI/CD pipelines, and access workflows
• Role or attribute-based access models to meet regulatory and compliance needs
• Built-in observability, lineage, and auditability across every layer

From encryption to access policies to change management, the goal is not just to build a functioning platform, it’s to build one that is secure, scalable, transparent, and adaptable.

Institutional Enablement Through Platform Architecture

The platform is not just infrastructure; it’s enablement.

Just like our data pipelines and models, our platform design is delivered with transferability and extensibility in mind. Clients should understand not only what was built, but why it was built that way. We provide documentation, walkthroughs, and governance artifacts to ensure internal teams can take ownership and confidently scale the solution.

The Value of Getting Architecture Right

A well-architected platform delivers performance, availability, and extensibility, without requiring retrofitted security down the line.

Trying to bolt on security after the fact is like forgetting the eggs when baking a cake: you can’t just fix it at the end. It needs to be part of the mix from the start.

At CTI Data, we design with this principle in mind. Whether it’s securing data movement through private networks or defining reusable deployment patterns for multiple layers of a Lakehouse, we believe the platform is the foundation of trust, and trust is what makes great data work possible.

Rick Ross is a Principal Consultant in our Data and Analytics Practice.