You Built the Pipeline. Now What? What Analysts Really Need from Data Engineers.

A finished pipeline isn’t the finish line. Technical delivery means little if analysts don’t use, trust, or understand what you’ve built. The secret to a successful data pipeline process is less about code—and more about context, clarity, and communication. 

The pipeline is built. So why isn’t it working? 

You’ve stitched together a dependable ingestion process. Transformed raw feeds into tidy tables. Maybe even rolled out a polished new data mart. 

Yet, something keeps getting lost between your work and the analysts it’s meant for. The same “quick fixes” land in your inbox week after week: Can you rename this field? Clarify that metric? Throw in just one more filter?  

The pipeline you architected with care isn’t lighting a fire under your analysts or end users. Usage fizzles. Meanwhile, those standing meetings meant for feedback? Mostly quiet screens and empty chairs. 

So what keeps your pipeline stuck in neutral? The real blockers aren’t technical, according to Zach Damuth, lead business intelligence consultant with RXA@OneMagnify. Building the pipeline is step one. Step two is making sure it’s understood, trusted, and seen as useful—by the very people it’s built for. The hard part is not the stack, but adoption and data usability for analysts and end users. 

If any of this feels painfully familiar, you’re not doing anything wrong. It means you’re building for real-world, messy situations. That’s something to be proud of, not apologize for. The next steps aren’t about rebuilding what works, but, instead, about making sure it works for everyone.  

What happens between ingestion and insight 

Making your pipeline work for everyone starts with understanding the frameworks behind pipeline management, according to Zach. Chances are, you’re already fluent in both:  

• Medallion’s bronze–silver–gold setup  
• Kimball’s classic stack: storage, data marts, and production-ready reporting layer 

Data pipelines often begin to fall apart right before delivering business value—usually during the handoff from technical execution to business impact, when data pipelines end up underused. The breakdown typically happens in the silver-to-gold phase of the Medallion architecture or from data marts to final reports in Kimball’s model. 

The core issue? Translation. The handoff between data engineers and analysts can be delicate. What analysts need from engineers is help speaking a common language. Analysts understand the needs of a business but may struggle to express them clearly to engineer. Meanwhile, engineers are ready to build anything—but “anything” can misfire if the specifics aren’t clear or get lost in translation. Miscommunication here can sink adoption. 

Both roles matter: Analysts frame the business need; engineers structure the solution.  

For example, when an analyst says, “I need year-over-year comparisons to see how individual stores are performing versus the entire group.” 

A sharp engineer hears: 

• Set up wide stack architecture 
• Add an n-series total for percentage comparisons 
• Build reporting tools that match these business questions 

Mastering frameworks is a start, but translating needs across the “last mile” determines if pipelines are adopted—or are ignored. 

The listening gap 

Most engineers can build exactly what’s asked—but sometimes what’s asked is different from what’s truly needed. The only way to close that gap is through better listening and real dialogue, not just ticket-taking. 

As Zach puts it, your job isn’t to become a business expert, it’s to pay attention. “I dedicate so much of my time in pipeline building to just having conversations with my subject matter experts on the other side of the fence,” Zach says. “Asking, ‘Okay, so what are you trying to do with this dashboard? Who’s the audience? How are they trying to use it?’” 

If you want to improve your pipeline’s data usability for analysts, here’s Zach’s playbook: 

Ask who you’re building for 
It all starts with understanding your audience. Instead of relying on the ticket or project brief, take a moment to ask, “Who’s going to use this data?” Knowing who you’re building for not only reveals what matters most, it also uncovers the real purpose behind requests.  

Pro tip from Zach: Sit down with stakeholders and find out what success looks like to them, not just the person writing the requirements. 

Dig into the data details 
Getting into the weeds is essential. Zach makes sure to run through every data set, line by line, with his analyst partners. “I need to know what defines a singular row in every piece of data you’re putting in front of me.”  

For instance, if you spot three columns called ‘record ID,’ don’t just assume they line up—ask whether they truly refer to the same thing, then let the response guide your technical decisions. This way, you avoid surprises later on and keep everyone on the same page. 

Clarify the process 
Knowing how the data lands in your lap isn’t enough. Ask about the process behind it: How is the data generated, touched, or updated? Zach suggests looking at workflows, or even how people are using Excel, to understand how your audience thinks about and interacts with data.  

“You should get to a point eventually where you are asking about the process. You need to understand the business operations,” he says. That perspective shapes how you set up transformations and ensures your work fits into the daily workflows of people using the data. 

Don’t assume anything—check your work 
Even if your analyst signs off on a data set, verification isn’t over. There’s always a chance something’s lost in translation between analyst and end user. “It’s just a perfect example of you need to take the time to listen, says Zach. “The onus is on you to ask that context…because they may not know to ask for it.”  

Ask for specifics about how the final recipient wants to view or use the data, and make sure what you deliver actually solves their problem. 

Learn about the business when it’s part of the task 
Nobody expects you to know every business process by heart. But when a new request comes in, make learning a priority. Treat each new use case as a signal to ask better questions, get subject matter context, and understand the why—not just the what—of the data.  

Engineers who approach the pipeline process with curiosity and open ears build solutions that stick. Every conversation you have, every question you ask—that’s what turns a pile of requirements into a tool people depend on. 

Building influence as a data engineer 

Let’s talk about the part no one says out loud: Most engineers know what “good” looks like. Clean pipelines. Reliable models. Sources documented and tested. That’s table stakes. The trickier bit is earning real buy-in so you don’t get trapped in an endless cycle of fixing the same issues and explaining your work. 

The truth is, a data product is only as valuable as the trust and excitement it builds. Without influence, adoption stalls and even your best pipelines collect dust. 

Influence isn’t just for PMs or executives—engineers can and should build it too. Zach’s approach is about finding quick wins and proving tangible value, even if all you have is your current context or a sliver of opportunity. 

Here’s how Zach builds influence for himself and his pipelines: 

1. Use the context you already have to deliver a win 
If access or time is tight, you don’t need to wait for a perfect spec. Zach says, “You need to find quick wins as a data engineer. Either that can be context you already have—great, I don’t need the access, and I already have a good idea of what I could build, and I could provide a quick win.” Start small, using what you already know to improve a workflow or save someone a step. 

2. Solve a specific problem for a specific, important person 
Sometimes the biggest impact is hyper-targeted. Take Zach’s client, whose CFO was wrangling a massive, intricate Google Sheet “spiderweb” just to keep the business running. The problem? It took up 15 hours of the CFO’s week just to maintain it. Zach focused on this one pain point, building a Domo pipeline that writes clean, ready-to-go data back into a Google Sheet.  

Instantly, the CFO got those 15 hours back—with accurate, organized data that lets him skip straight to the real work. Obviously, a data pipeline that lands in Google Sheets isn’t the end goal. But in this case, solving a real problem for a key stakeholder helped open more doors—and kept pipeline projects moving forward. 

3. Find and pitch time-savers 
Nothing opens doors like returned time. Zach explains, “Time-savers—those are the things that resonate the quickest. I’m not trying to take over anyone’s job. Let me help you do your job better. If I can save you ten hours a week, just let me help you build this reporting structure.”

Frame your ideas as ways to make people’s days easier, not as attempts to replace them. This is how you get people to listen, try new things, and start spreading the word about your work. 

That’s how influence is built: small, well-timed wins that show instead of tell. Each success builds trust, making it easier for you to get buy-in—and turn your pipelines into products people actually love. 

Build what lasts 

A good pipeline moves data, but a great one moves decisions. If you want your work to stick, it’s less about flash and more about empathy, clarity, and trust. 

Every new question you ask, every quick win, and every effort to bridge the gap between business and technical teams isn’t just maintenance—it’s the foundation for real influence and adoption. 

Domo helps you build data pipelines that aren’t just technically sound, but actually make a difference. Governed, usable, business-ready data means less rework, more impact, and pipelines that truly last.