Findings from 104 data professionals on data team size, reporting lines, and challenges
Introduction
Each month, we run a brief survey through our newsletter and social channels to capture what’s top of mind for today’s data teams. The goal is to share actionable benchmarks and peer-driven insights that can help you fine-tune your data stack. These reports aim to reflect the shifting priorities and real-world challenges faced by engineering, analytics, and operations professionals.
This month, we focused on data team structures and roles. As demand for data-driven insights grows, organizations grapple with how best to staff, organize, and align their data functions. From hiring and reporting lines to cross-functional responsibilities, how teams are built can significantly impact efficiency and outcomes.
To better understand how companies approach these questions, we sent out a survey to our subscribers, with 104 data professionals submitting responses.
Why This Survey Matters
The shape and composition of modern data teams are evolving quickly. What was once a small group of analysts generating reports has expanded into cross-functional teams of engineers, scientists, and product managers who own everything from ingestion to BI.
Our findings align with the dbt Labs 2025 State of Analytics Engineering report, which highlights that analytics engineers are no longer niche but increasingly central to the modern data function. dbt’s research shows that AI-driven investment and broader adoption of analytics engineering practices are fueling team growth and reshaping responsibilities.
This signifies a clear industry shift: data teams are not just getting larger, they are becoming more specialized and hybrid, blending technical depth with business alignment. For leaders, the implication is that success will depend on designing teams that balance centralized efficiency with embedded, stakeholder-facing roles.
Methodology
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Respondents: 104
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Company sizes: Ranging from early-stage startups to global enterprises
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Industries: SaaS (42%), financial services (18%), retail/eCommerce (15%), healthcare (11%), media/entertainment (7%), manufacturing (7%)
Key Takeaways
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42% of data teams report into CTO/Engineering, while 26% report to a Head of Data
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Hybrid structures dominate (42%), followed by centralized teams (37%)
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Hiring capacity (32%) is the top challenge, followed by cross-team alignment (27%)
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21% reported major restructuring in the past year, while 35% added new roles
Reporting Lines
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Insight
Engineering is still the most common reporting line, but more than a quarter of teams now report into a dedicated Head of Data, signaling a shift toward more strategic oversight.
Structure Models
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Insight
Hybrid models have overtaken pure centralized approaches, as organizations try to balance efficiency with embedding analysts closer to the business.
Current Challenges
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Insight
Headcount and alignment remain the biggest barriers to impact. Even with modern tooling, organizational challenges outweigh technical ones.
Organizational Change
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Insight
More than half of data teams changed their structure in the past year, either by restructuring or adding roles. This highlights how dynamic and fast-changing the function is across industries.
Final Thoughts
Data teams are evolving rapidly, with lean core teams expanding into hybrid models that mix centralized engineering with embedded analysts. Roles are diversifying; analytics engineers and data product managers are rising, while challenges like hiring capacity and alignment remain constant.
The big picture: companies are still experimenting with how best to staff and structure their data functions. Those that balance centralization, business alignment, and role diversity are most likely to unlock data’s full strategic value.
About Integrate.io
Integrate.io helps data teams of all sizes reduce overhead and accelerate delivery by unifying ingestion, transformation, and orchestration into a single low-code platform. By consolidating tools and simplifying pipelines, we make it easier for lean teams to operate like much larger ones.
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