Designing secure data solutions isn’t inherently difficult. But implementing them in a modern enterprise? That’s a different story. It requires collaboration across data engineering, infrastructure, identity, security, DevOps, and cloud operations teams—each with their own priorities, vocabulary, and processes.

We’ve seen this disconnect play out time and time again: a data engineering team engages the right groups—identity and access management, security, DevOps, cloud infrastructure—but coordination stalls. Requirements aren’t fully understood, approvals are delayed, and documentation gets lost in translation. In the rush to move forward, engineers often fall back on insecure patterns like hardcoded credentials or shared service accounts with static passwords.

These shortcuts might get pipelines running, but they introduce a real security risk.

 The Real-World Complexity Behind Secure Architecture

Take a common example: a customer using Snowflake. It’s not unusual to see multiple identity and storage integrations—SAML for single sign-on with Azure Entra ID, SCIM for user and group provisioning, multiple OAuth 2.0 configurations, and storage integrations with AWS or Azure.

On paper, Snowflake’s documentation outlines how to configure these components. But in practice, it assumes a single persona handles every step—from enterprise app creation in Azure to Snowflake role mapping. That’s rarely the case. Instead, tasks are spread across siloed teams and managed through ITSM platforms like ServiceNow or Jira, where interactive collaboration is limited.

A simple SAML integration request turns into a game of telephone between Snowflake admins and identity platform owners. Even straightforward tasks like exchanging metadata can become tedious. SCIM configurations add complexity—token management, SAML attribute mapping, and naming standards for security groups (which ultimately become roles in Snowflake).

And that’s just identity. When teams begin working on OAuth 2.0 integrations or private link storage access, architectural clarity becomes critical. Cross-team collaboration must go deeper. Diagrams need to be created, security reviews scheduled, and assumptions aligned. But coordinating across fragmented teams is difficult. Ownership is often unclear, and the pressure to “just get it working” is strong.

Why Good Engineers Sometimes Make Risky Choices

Most data engineers aren’t trying to cut corners—they’re trying to meet delivery goals. Their priority is building reliable pipelines that ingest, transform, and publish data for analytics consumers. When cross-team dependencies slow progress, insecure workarounds like static credentials start to look like the path of least resistance.

But this approach doesn’t scale and falls short of modern security standards. Fortunately, Snowflake has announced the deprecation of basic username/password authentication, which will push many organizations to revisit these practices. That’s a good thing.

So, How Do We Get It Right?

It starts with alignment. Before implementation begins, stakeholders need to come together to define:

• System scope – What cloud platforms, geographies, and source/destination systems are in play, and where are they located?
• Analytics landscape – What tools and personas will consume the data?
• Data flows – How does data move between zones?
• Data privacy requirements – What’s the sensitivity of the data, and what are the requirements for handling, storing, and accessing it?

From there, a clear architecture can be developed. It should include identity integration, role design, security enforcement points, and data movement flows. Most importantly, it needs cross-functional buy-in—including from security.

Adjustments will be needed. Role definitions may need refining. Group naming standards may need to align with internal policies. Identity governance may need to evolve. These conversations take time, but they’re essential to avoid costly security rework—or worse, insecure deployments.

Implementation Is Where Many Teams Still Struggle

Even with a solid design, the final mile is often the hardest. When implementation tasks land across multiple enterprise teams, gaps in understanding can surface. Secure private networking in Azure or AWS? DNS forwarding? OAuth redirect URIs? These details often fall outside the daily responsibilities of those teams. When questions arise, data engineers are called in—but often lack the deep infrastructure context to provide answers.

At this point, many organizations lean on vendor support—only to discover that help stops at the platform boundary. Vendors will assist with their own configuration, but not with cloud networking, IAM policies, or Azure-specific app settings.

The Good News: You Don’t Have to Go It Alone

What we’ve outlined here isn’t hypothetical. We’ve seen it play out across dozens of enterprise clients—each with their own org charts, naming conventions, and approval workflows. Most teams don’t get a second chance to master this process. They build the foundation once, then move on. Any cracks in that foundation might not surface until they become a real problem.

At CTI Data we partner with you to build secure, highly available, strong foundations aligned to your specific requirements. We specialize in helping teams bridge the gaps by acting as translators across enterprise teams, applying repeatable patterns. We provide hands-on support and architectural clarity to help clients implement secure, scalable, and highly available data solutions—without the guesswork.

Rick Ross is a Principle Consultant in our Data and Analytics Practice. Contact us today to learn more about our data engineering and security solutions and services.