Struggling to bridge the gap between data engineers and business analysts in a data warehousing project?
Curious how to connect data engineers with business analysts effectively? Share your strategies for seamless collaboration in a data warehousing project.
Struggling to bridge the gap between data engineers and business analysts in a data warehousing project?
Curious how to connect data engineers with business analysts effectively? Share your strategies for seamless collaboration in a data warehousing project.
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Oh, totally get that—it’s like they’re speaking different languages sometimes! What I do is set up regular syncs where both sides can align on goals and expectations—keeps everyone on the same page. I also create some shared documentation—translating tech jargon into business terms—so there’s less back-and-forth. It’s all about communication—making sure we’re building solutions that actually solve business problems, not just cool tech stuff.
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In a world where opinions and narratives often clash, taking the time to listen to the other side of a story?fosters understanding, promotes fairness, and enables us to make informed judgments. Data Engineers make effort to understand Business & Business Analysts spare time to gain nuances of Data Engineering. I have seen success when this environment is created.
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One approach is having data engineers focus on enterprise data domain models in the data layer. I recommend keeping it agnostic to source system, domain focused so it static when replacing upstream operational systems and sources. Business analytics I would have focused on having a lead per semantic model for development and support. BU/domain specific reports would be created off the business semantic model.
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In my recent data warehousing project, I faced the challenge of bridging the gap between data engineers and business analysts. The engineers focused on building efficient data models, but these didn’t always align with the business requirements analysts needed for reporting. To overcome this, I took on the role of a translator between the two teams. By simplifying the language, fostering iterative collaboration, and ensuring regular check-ins, I helped create business-friendly data models that met both technical and business needs.
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To simplify this, both are different roles at all. One thinks of raw data to transform into meaningful anthe then the other thinks of getting useful insights to make business profitable.
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