Data Products Should NOT Be Built by Data Engineers!

Let’s ruffle a few feathers.

Too often, data engineers are tasked with building business facing assets such as dashboards, reports, and decision tools. This is now extending to data products. But these lie far outside their core responsibilities. This blog challenges that outdated model. We argue that engineers should focus on building scalable data infrastructure, not interpreting business needs. The real value of a data product comes from domain expertise, not just clean pipelines, data, or code. With new AI-powered platforms like Latttice, based on Data Mesh principles, businesses can decentralize data product creation — putting control in the hands of the domain, and those who understand the questions and need the answers.

Why are we still doing this?

Somewhere along the journey to becoming “data-driven,” we lost the plot. We told data engineers to build pipelines and analytics, then also told them to build reports, dashboards, and data products for business teams. We abandoned functionality that empowered the business user or domain expert, and focused on delivering ever increasingly complex technology and processes.

“Data engineers shouldn’t be doing business analytics. They should be building the foundation that enables it.”— Ben Rogojan, Seattle Data Guy

It’s not only a waste of their time — it's also a misuse of talent.

Data engineers are phenomenal at setting up scalable, secure pipelines and platforms. But when it comes to defining what the business truly needs from data, they’re not the ones talking to customers, managing logistics, or forecasting revenue.

We need to stop expecting data engineers to do everything — data modeling, reporting, stakeholder management. It's unsustainable and unscalable.”— Shachar Meir, Data & AI Thought Leader Data Products Are Business Products

A data product is only valuable if it answers the right question at the right time, for the right person. That insight doesn’t come from perfect SQL or optimal schemas — it comes from context.

“Data products are not just datasets or dashboards. They are experiences tailored to business needs.”— Barr Moses, CEO of Monte Carlo

Who has that context?

• The marketing lead running 12 campaigns trying to prove ROI

• The ops manager needing visibility across the supply chain

• The finance director monitoring margin fluctuations

  
  

These are the people (and their teams) who should be building the data products.

“The business value of data only emerges when those closest to the decision are empowered to work with it directly.”— Sol Rashidi, Former CDO, Estée Lauder

So, what should data engineers be doing?

Data engineers should focus on building robust, scalable data platforms — making sure data is accessible, secure, and clean. Their role is to enable data product creation, not own it.

“The best data platforms fade into the background. The users see answers, not architecture.”— Tristan Handy, Founder of dbt Labs

Business teams should not be waiting weeks or months for data. They should have self-service tools to build and evolve their own data products that power their own data assets.

“Your ops lead shouldn't wait two weeks for a dashboard. Give them tools to create one in two hours.”— Joe Reis, Co-author, Fundamentals of Data Engineering

Enter the Era of the Domain Data Owner

This shift is already happening — and one of the platforms leading this shift is Latttice.

What is Latttice?

Latttice is an AI-powered, zero code, data product platform built from the ground up on Data Mesh principles. It empowers domain data owners — not just engineers — to create, manage, govern, and share their own data products without writing code.

With Latttice, business teams can:

• Instantly access trusted data. Stop hunting. Start using. Users can discover and consume data products with confidence—secure, governed, and instantly usable

• Create Data Products, No Code Required. Empower any team, in any business domain, to create and share valuable data products without needing technical skills

• Works with what you have. From AWS to Azure, on-prem to cloud, dashboards to machine learning—Latttice integrates effortlessly with a user’s ecosystem.

• AI at the core. Utilize AI to automate discovery, relationship mapping, governance recommendations, and product creation—all in a seamless experience.

  
  

As I’ve said often, the future of data products isn’t built by engineers — it’s owned by the people asking the questions. At Data Tiles, we’re here to make that shift a reality.

“Domain-driven ownership of data products is essential. It brings accountability, clarity, and faster delivery.”— Zhamak Dehghani, Creator of Data Mesh

Instead of centralizing analytics in overburdened data teams, Latttice decentralizes control while ensuring governance and quality. It gives the people closest to the problem the power to build the solution — responsibly and independently.

Stop the Madness

Let’s stop asking engineers to solve business problems they don’t live and breathe.Let’s stop dragging out data requests because they’re stuck in technical queues.Let’s stop pretending that the person writing the code should also write the story.

We didn’t build Latttice to replace data engineers — we built it to free them. When domain experts can create their own data products, everyone moves faster, and insight becomes a team sport.

“Data is a team sport — engineering, analytics, and business must play together. But let the experts do what they do best.”— Cassie Kozyrkov, Chief Decision Scientist, Google

Because in the end, the best data product is the one that gets used — not the one that was coded in a silo.

Want to see how this works in practice?

Explore how Latttice empowers domain owners to build and govern their own data products. Zero code. Real control.

Cameron Price.

References

1. Ben Rogojan (Seattle Data Guy). Is It Time to Say Goodbye to Data Engineers?

2. Shachar Meir. Things Data Engineering Teams Shouldn’t Be Doing Anymore.

3. Barr Moses. How to Treat Your Data as a Product. Monte Carlo Blog.

4. Sol Rashidi. AI Strategy and Business Transformation Commentary.

5. Tristan Handy. dbt Labs Thought Leadership.

6. Joe Reis & Matt Housley. Fundamentals of Data Engineering. O’Reilly Media, 2022.

7. Zhamak Dehghani. Data Mesh: Delivering Data-Driven Value at Scale. O’Reilly Media, 2022.

8. Cassie Kozyrkov. Decision Intelligence Thought Leadership.