AWS Public Sector Blog

How Rize Credit Union built a serverless data lake on AWS to become its own source of truth

How Rize Credit Union built a serverless data lake on AWS to become its own source of truth

Credit unions exist to serve members, not to run data centers. Every hour our team spends on infrastructure is an hour not spent on the question a member actually cares about: “Is my money safe, is my experience easy, and is my credit union on my side?” Moving to a serverless, managed-service foundation on Amazon Web Services (AWS) means our engineers spend their time on membership modeling, fraud analytics, and AI augmentation instead of capacity planning.

When I joined Rize Credit Union as chief technology and innovation officer, our data looked like most $1.5B credit unions’ data looks: everywhere and nowhere at the same time. Structured records lived inside our Jack Henry Symitar core. Contact-center interactions sat inside Talkdesk. Survey results, vendor extracts, and one-time files lived on shared drives and in individual inboxes. Every report was a negotiation, and every “source of truth” had a footnote. This post is the story of how a small team used AWS managed services to replace that sprawl with a single, serverless, data lake built for the cloud, and why the architecture matters more than any one dashboard it produces.

A Windows-centric shop with no center of gravity

Credit unions are vendor-dependent by design. Symitar alone supports nearly 700 credit unions, and around that core sits a constellation of point solutions for lending, payments, servicing, digital banking, and member experience. At Rize, that meant structured and unstructured data scattered across systems we didn’t own, on cadences we didn’t control, with semantics that didn’t agree. We had no centrally reliable place to ask a question and trust the answer.

The deeper issue was due to organizational physics rather than a technical issue. A two-person data engineering team can’t overcome that kind of entropy by managing more servers. We needed leverage. We needed our people focused on serving members, not patching virtual machines (VMs). And we needed an architecture that a small team could actually reason about, change safely, and extend without rewriting.

A serverless, managed, cloud-based solution

We built the data lake entirely on AWS using a template for a credit union lake, and we followed a deliberate rule: no long-running compute and no self-managed servers. Every component had to be a managed service we could define in code and then leave to function automatically. The backbone includes:

Everything is managed through Terraform across three repositories—core data lake, networking, and platform—to enhance portability and resiliency. For a learning team, infrastructure as code (IaC) is a best practice as well as a safety net that makes speed possible.

Investing in solving the credit union data model

One architectural detail deserves its own paragraph, because it trips up every vendor we talk to. Traditional banks use a data structure based on products: a checking account is the top-level entity, to which all other account information is connected. Credit unions add a parent layer called the membership. People attach to memberships. Products (such as shares, loans, or cards) attach to memberships. People can also attach directly to products. A single member can hold multiple memberships, each with multiple products, creating a many-to-many web that is substantially more complex than the product-centric model most off-the-shelf analytics tools assume. A meaningful share of our engineering effort went into decoupling and normalizing those relationships so the data is high-fidelity and consumable downstream.

OpenMetadata for governance

The AWS Glue Data Catalog is excellent for technical metadata, but most of our nontechnical staff aren’t going to sign in to the AWS Management Console to find a table. We deployed OpenMetadata on top of the lake as our governance layer and search surface. Business users search for datasets by keyword. Every gold-layer column carries field-level documentation, including the specific values a field can take. A “transaction category” column advertises fee, ATM, transfer, payer to payer (P2P), Automated Clearing House (ACH), and payroll instead of forcing someone to guess. Personally identifiable information (PII) is tagged at the column level, which means if a vendor ever has a breach, we can scope our exposure in minutes instead of weeks. Ownership is assigned to teams, so data quality alerts route to the people who can actually fix them rather than piling up in my inbox.

How AWS helped—beyond the services

I use AWS for everything, and I want to be specific about what “everything” means, because the services are only half of it. The other half is the people. Solutions architects, specialist SAs, and the conversations I have at the annual AWS re:Invent conference and the AWS Summits throughout the year have shaped this architecture as much as any documentation page. When we hit the credit union membership modeling problem, when we debated Iceberg versus Hudi, when we weighed a hub-and-spoke network topology against alternatives—those decisions were sharpened in whiteboard sessions with AWS architects who had seen the patterns before. For a small team trying to punch above its weight, that access is a force multiplier.

Results

The results we generated have increased efficiency and resiliency. They include:

  • One source of truth – Data from Symitar—Advanced Reporting for Credit Unions (ARCU) and a near real-time Software-Defined Storage (SDS) replica—Talkdesk, vendor extracts, and APIs now land in a single S3 bucket daily, after core end-of-day balancing provides general-ledger integrity.
  • Zero standing compute – The entire pipeline is serverless. There is no cluster to babysit, no VM to patch, no license to administer.
  • Resumable, lossless loads – CDC plus the DynamoDB state tracker means a failed job picks up from the exact log sequence number it left on with no duplicates or no gaps.
  • Storage savings through Iceberg – Our legacy approach stored a full daily snapshot of every table. Iceberg only records what changed, dramatically shrinking the storage footprint while preserving a full history of the records from creation forward.
  • Built by two engineers – The platform currently running daily analytics for a $1.5B institution is maintained by a two-person data engineering team. That ratio is the outcome that matters most.

What’s next

The architecture was designed for the next phase, not only the current one. The CDC infrastructure and Iceberg tables already support real-time ingestion, but we haven’t had a business case that requires sub-daily data yet. Event-driven integration logs from our frontend applications will be added to the lake next, enabling fraud analytics and proactive member notifications. OpenMetadata’s field-level documentation will ground an AI layer so natural-language queries run against accurate, governed context instead of hallucinated schemas. We’ll consolidate from US-West (N. California) – us-west-1 into US-West (Oregon) – us-west-2 AWS Regions to eliminate cross-Region transfer costs. We’ll split our current single-account environment into dedicated production and development accounts with fully automated continuous integration and continuous delivery (CI/CD).

If you’re a credit union CIO or CTO staring at the same sprawl we started with, my call to action is straightforward: don’t try to overcome entropy by force. Pick a serverless, managed-service foundation, define it in code, and let a small team do the work. And don’t wait a decade to decide. As Andy Jassy puts it, “Speed is not preordained—it’s a choice.” You can’t flip a switch and suddenly have it; you have to architect it into the organization by building a culture of urgency and a willingness to experiment. Speed is something you do, not something you inherit. That’s the move. We’re happy to compare notes.

To learn more, visit Rize Credit Union. To explore a digital transformation for your credit union, connect with the AWS team. This specialized team delivers flexible cloud solutions to help credit unions better serve their members.

George Estrada

George Estrada

George Estrada is a fractional Chief Technology & Innovation Officer at Rize Credit Union and the founder of Orion Digital Services, a science-based consultancy firm focused on future readiness and AI strategy. He is completing an MS in Complex Systems Science at Arizona State University and writes on technology strategy on LinkedIn.