If your data engineering team feels overwhelmed, with unclear roles and constant firefighting, chances are the team structure is broken. Modern data challenges demand more than just throwing bodies at problems. Getting the right structure unlocks faster delivery, better data quality, and less frustration.
Real-world experience and foundational texts like Fundamentals of Data Engineering emphasize that clear role definitions and collaboration models are essential. From platform engineers who build and maintain infrastructure, to analytics engineers focused on transforming data, and data product owners who keep business needs front and center—the right division of labor is the backbone of an effective team.
Why structure matters
Expecting one team or person to own everything, from pipelines and data lakes to modeling and stakeholder alignment, quickly leads to chaos. As your data landscape grows, so does the need to specialize. Trying to do it all under a generic “data engineer” label creates friction and bottlenecks.
Good structure clarifies who builds the pipes, who curates the data, and who prioritizes work. It’s also a communication accelerator: when analytics engineers are embedded with product teams, they get faster feedback and produce more relevant insights. Platform engineers, freed from context-switching, focus on reliability and scalability.
In addition, clear team boundaries help manage growth. Without structure, doubling your data volume or adding new business units can cause a collapse of workflows. Structure provides a framework to onboard new team members efficiently and reduce duplicated efforts.
Proven patterns in the wild
dbt’s team model champions a hybrid approach: a central platform team builds reusable data infrastructure, while analytics engineers work embedded within business units. This avoids both over-centralization and duplication of effort. Embedded engineers stay close to the product and understand business context deeply, while the platform team enforces standards, builds tools, and maintains reliability.
Shopify Engineering’s journey mirrors this. They initially had a centralized data team but realized this created bottlenecks. By splitting into platform and embedded roles, they improved delivery speed and trust from stakeholders. The platform team focuses on scalable pipelines and tooling, and embedded engineers tailor data models for specific products and teams.
Other successful teams echo this balance. Too much centralization means slow turnaround and frustrated business users. Too little, and you risk inconsistency, duplicated work, and fragmented data quality.
Common pitfalls to avoid
A classic trap is ambiguous roles where “data engineer” means everything. This overloads teams and blurs accountability. Infrastructure gets neglected, pipelines break, and delivery slows. Engineers end up firefighting infrastructure problems instead of enabling data consumers.
Another frequent failure is over-centralization. When one team is gatekeeper for all data, requests pile up, and the organization loses trust. Business teams start going around the data group, creating shadow analytics and splintered data sources.
Unclear ownership also worsens technical debt. When no one is clearly responsible for data quality or pipeline health, fixes get delayed, bugs accumulate, and scalability suffers. This creates a fragile system where people hesitate to make changes, fearing they’ll break something essential.
On top of that, failing to embed analytics engineers near business units slows feedback loops and reduces the relevance of insights. The communication overhead and context gaps frustrate both data teams and stakeholders. Add to this the chaos of skipping tooling standardization, where teams reinvent pipelines, skip testing, and struggle with deployment: it’s clear how quickly progress can stall.
Best practices for your data engineering team structure
Start by defining roles clearly, matching them to your company’s scale and needs. Platform engineers own infrastructure and pipelines, ensuring reliability, scalability, and automation. Analytics engineers focus on transformation, modeling, and business-facing data products. Data product owners prioritize requests and keep business alignment tight.
Embedding analytics engineers within product teams is a powerful accelerator. It improves communication and responsiveness, allowing data products to evolve quickly and accurately. Meanwhile, a centralized platform team focuses on core infrastructure, tooling, and enforcing standards, preventing fragmentation and duplication.
Standardizing on tools like dbt for data transformations ensures version control, testing, and documentation—all essential for scaling. Orchestration tools like Airflow automate pipeline scheduling and monitoring, improving reliability.
Here’s a simple example Airflow DAG running dbt daily, showing how teams can automate reliable workflows:
from airflow import DAG from airflow.operators.bash import BashOperator from datetime import datetime with DAG('daily_dbt_run', start_date=datetime(2025, 1, 1), schedule_interval='@daily') as dag: dbt_run = BashOperator( task_id='dbt_run', bash_command='cd /usr/app && dbt run' )
Monitoring and alerting should be baked into pipelines early, so teams catch issues before stakeholders do. Regularly reviewing team structure as the company grows helps spot emerging bottlenecks.
Moving forward
Data engineering teams don’t need to reinvent the wheel but do need to rethink how they organize. Clear roles, a hybrid structure, and standardized tooling reduce friction and technical debt. That lets teams focus on what matters: delivering timely, trusted data that powers your business decisions.
Invest in platform reliability first, then scale analytics embedding. Foster a culture of ownership and continuous improvement. Remember: structure is a tool, not a constraint. Use it to empower your team, not box them in. With the right setup, you can create clean analysis and dashboards 10x faster. Try Briefer for free!