The unicorn data engineer doesn’t exist - but the balanced data team does.
Between 2023 and 2025, I saw job posts that wanted everything. Python, Scala, dbt, Airflow, Spark, Kafka, Terraform, and “bonus: machine learning experience.” One person. One budget. Zero applicants. Good luck.
That era is ending. The companies hiring well in 2026 hire for depth in one area and build breadth through team composition.
What works: a platform engineer who owns infrastructure. An analytics engineer who owns the data models. An ML engineer when you actually have ML use cases. Three different people, three different skill sets.
T-shaped profiles. Deep in one domain, broad enough to collaborate across others. You don’t need everyone to know everything - you need the team to cover everything.
The senior talent pool is shrinking. I’ve seen the same candidates show up at four different clients. Fighting over the same five people isn’t a strategy.
Build a pipeline instead. Hire one senior, two mid-level. Let the senior set standards. Let the mid-levels grow into them.
How many months has your longest-open data role been posted?