J.Crew is an iconic American fashion brand known for timeless style and modern classics. Like many established retailers, J.Crew is evolving to meet the needs of today’s consumers and to compete in a digital-first market.
But J.Crew’s prior data infrastructure created significant challenges for the business to move both quickly and scale. Built on SAP BW, the 15-year-old infrastructure had become a bottleneck with fragmented workflows that slowed down decision making.
J.Crew chose to rebuild its data foundation with dbt Labs. Making matters more complex, the migration was set to occur during the holiday season, at the moment when orders would double or triple. The stakes were high for J.Crew to modernize, fast.
To meet its aggressive timelines, J.Crew partnered with the dbt Labs Professional Services team.
Getting a complex data transformation right
J.Crew’s data infrastructure had begun to reach its limits. The infrastructure’s fragmented database structure meant that each engineer worked in a siloed database. This led to frequent permission issues, inconsistent access across environments, and friction that slowed down data insights.
When J.Crew decided to work with the dbt Labs Professional Services team, it was just six months before the holiday season. Certain systems, like order processing, would see data volumes multiply. Any issues during the migration would have a significant impact during a critical reporting window.
At the same time, J.Crew’s data team was new to using dbt and unfamiliar with workflows like Git version control and pull request reviews. Without a clear strategy for project structure, they risked losing weeks to researching and debating best practices for modern architecture.
“We were essentially jumping 15 years forward in time to a really modern stack,” says Nick Leonard, Director of Data Engineering at J.Crew. “Migrating away from a legacy environment that’s been there that long requires a lot of thought to get right. We knew we needed all the help we could get.”
Onboarding with expert guidance
To navigate the transformation, J.Crew partnered with dbt Labs’ Professional Services. The first step of onboarding involved setting up the environment and hands-on training.
When it comes to onboarding, dbt Labs takes an applied learning approach. Rather than sit through lengthy lectures, participants work on a real use case within their environment and follow a curriculum designed to match their experience level.
With the guidance of a Technical Instructor, J.Crew’s data team learned the fundamentals of dbt while transforming their own data. By the end of the training program, the data team had a production-ready implementation, the skills to migrate other use cases, and the confidence to scale their data transformation. In fact, the success of the initial cohort led J.Crew to invest in a second package to train a new group.
“The training exceeded my expectations and helped accelerate enablement across the organization,” Leonard emphasizes. “The dbt Labs Instructor was really fun, personable, and helpful for getting people familiar with the platform.”
Following the training, the data team partnered with a dbt Labs Resident Architect to scale their capabilities ahead of the holidays. The Resident Architect acted as a strategic technical partner and integrated directly into the team: joining weekly standups, providing hands-on guidance with Git workflows and code reviews, and advising on which dbt features to adopt (and when to wait).
"The Resident Architect showed us what was possible.” Leonard highlights. “Their insights were invaluable for making the most of our investment in dbt.”
In short, the Resident Architect helped the team make opinionated, informed architectural decisions during a critical time frame. In just three months, the Resident Architect:
- Streamlined database architecture, resolving permission errors and simplifying development by consolidating per-developer environments.
- Advised on project structure and scalability, including when to mesh dbt projects and how to design semantic layers.
- Educated the team on emerging dbt features like webhooks, contracts, and unit testing, and showed how to use them effectively.
- Provided documentation to ensure knowledge transfer with current and future team members.
- Constructed decisions with cost in mind, especially as it relates to compute and warehouse uptime.
“The Resident Architect gave us clear recommendations based on what we had and where we wanted to go,” reflects Leonard. “They helped us think through the tradeoffs for using dbt for certain things, like semantic layer, orchestration, monitoring, alerting, and scheduling.”
An accelerated implementation with long-term confidence
By partnering with dbt Labs Professional Services for onboarding, J.Crew followed a clear, expert-led path. With this guidance, the data team eliminated the guesswork and realized value from their investment faster. Notably, they launched key pipelines during their highest-volume season without disruption.
“Overhauling and streamlining our database structures and permission requirements was much more straightforward with the Resident Architect’s guidance,” highlights Leonard. “The impact was immediate. Our setup had been causing missing objects and permission errors, but after consolidating, our developers were unblocked within a week.”
The engagement also led to smarter, more cost-effective decisions. The Resident Architect helped the team avoid over-engineering early in the process. Now, the J.Crew data team is clear on what they’re spending and are on a sustainable path, without unexpected compute costs.
“We saved at least a couple of months and onboarded 50% faster during an aggressive implementation timeline,” says Leonard. “We had pipelines in production running with no issues or 3am calls that something is broken.”
Enabling AI-readiness
Most importantly, the team is confident in what they’ve built with dbt and their ability to advance the business with timely insights.
“We’ve established a solid foundation, thanks to the expertise we got from the get-go,” Leonard affirms. “It’s clear to me how dbt fills the gaps that I’ve seen in data engineering my entire career.”
Modernizing J.Crew’s data foundation was just the beginning. With cleaner data models, streamlined processes, and greater cost transparency, the data team is excited to drive J.Crew’s next era with AI.
“From day one, working with dbt meant we were making our data AI-ready,” concludes Leonard. “We’re building an ambitious roadmap around AI, and it’s backed by a foundation we can trust with dbt Labs.”
