Hire a Data Engineer
The engineer who owns the pipes your analytics and ML run on.
Most data engineers are trained inside a data team
- Retained recruiters.
- LinkedIn Recruiter seats.
- Keyword-matched resumes.
- Templated InMails.
Big-company data engineers are specialists: one owns ingestion, another owns warehousing, another owns orchestration. They're optimized for one slice of a mature data platform.
At a startup, you need someone who can design the whole stack. They're picking the warehouse, setting up DBT, wiring Airflow, instrumenting events, and coaching the rest of the team on data hygiene.
That generalist-owner profile is rare and hard to source through keyword searches for 'Senior Data Engineer.'
How Superposition finds your data engineer
Generalist-owner signal
We find engineers who've built data stacks from scratch: side projects, startup experience, or open source contributions to data tooling.
Stack-aware intake
Our voice agent captures the specific shape: real-time vs. batch, warehouse vs. lakehouse, analytics-first vs. ML-first.
Finds builders, not specialists
Passive candidates shipping real data infra, not career specialists looking to slot into a mature team.
Flat-rate pricing
$500/agent/month. No percentage fees.
Questions.
Answered.
A data engineer owns the pipelines and infrastructure. An analytics engineer owns the models and metrics on top. Tell us which problem you have and the agent weights accordingly.
Usually when analysts or ML work is blocked on data that arrives late, wrong or not at all. Before that, the person who built the product can usually keep up.
No. Warehouse and orchestration tools change constantly; understanding of data modelling, idempotency and late-arriving data does not.
Through evidence of failure handled: write-ups about a pipeline that broke silently, tests on data, backfills they ran. That is the signal, not the tool list.
Hire a Product Designer
The designer whose work defines how your product feels.
Hire a Forward Deployed Engineer
Half engineer, half field operator. The hardest hybrid to hire, and the one every AI company now needs.
Superposition vs. Juicebox
Juicebox makes profile search faster. Superposition takes responsibility for the recruiting work around the search.
Find your data engineer.
Get a builder who can design the stack, not just operate one.