2026 State of Analytics Engineering Report
Analytics engineering has entered a new phase. AI is no longer experimental inside data teams — it's embedded in daily workflows, funded by leadership, and actively reshaping how code is written and insights are delivered.
But acceleration is outpacing governance. The 2026 State of Analytics Engineering Report captures exactly where the gaps are and what high-performing teams are doing differently.
What's inside:
AI adoption is embedded, but governance is lagging
72% of teams prioritize AI-assisted coding, but only 24% prioritize AI-assisted pipeline management (testing, observability, quality controls)
Trust is now a top strategic priority
Importance of data trust jumped from 66% to 83% year-over-year, outpacing speed (50% → 71%) and cost reduction
Hallucination risk is real
71% of respondents are concerned about incorrect or hallucinated data reaching stakeholders
Infrastructure costs are outrunning budgets
57% report increased warehouse and compute spend, compared to only 36% reporting increased team budgets
The bottleneck has shifted
Technical integration challenges are declining (35% → 27%), but data ownership ambiguity and quality gaps persist at nearly unchanged rates
Whether you're leading a data team or building inside one, the 2026 report gives you a clear benchmark on where the field is and where discipline in the next phase actually pays off.