Hire senior data engineers and data scientists.
Vetted data engineering, data science, and ML talent matched in 72 hours. Transparent per-builder pricing. Working builder embedded in two weeks under your team's day-to-day management.
$100+/hr
Senior US data contractor starting rate
72 hrs
From scoping call to curated shortlist
~2 weeks
From scoping call to working builder
3-to-5 months
Typical FTE search timeline for a senior data engineer. By the time the hire is closed, the dashboard nobody trusts is still in production.
We thought we needed a data scientist. We actually needed a data engineer to stop the pipeline from breaking twice a week. Once the data was reliable, the science could begin.
The clearest engagements have a problem with the data, not a job description.
A pipeline that breaks twice a week and analysts can't trust the numbers. A warehouse where the dashboards take 90 seconds to load. A predictive model that has to ship with an evaluation loop. A business decision that needs a real analysis behind it, not a chart. Senior data work in 2026 isn't 'help us understand our data.' It's deciding when the problem is engineering versus science, when the model matters less than the data feeding it, when the question itself needs to be sharpened before any method makes sense.
What most teams try
Hire a data scientist to fix a data engineering problem
If the pipeline is breaking and the data is unreliable, no amount of statistical rigor compensates. The hire spends month one cleaning data rather than analyzing it; the team spends month three wondering what they paid for.
Hire a data engineer for an unclear analytical question
The pipeline gets built and the data flows. Nobody acts on it because the question wasn't sharp enough to begin with. The engineering hire was correct; the scoping wasn't.
Post 'data engineer' and accept whoever applies
Pipeline builder, warehouse engineer, and ML data engineer are three different profiles. The shortlist optimizes for the JD, not the actual problem. Mismatches surface in month two.
What A.Team brings
Vetted senior data engineering and data science talent under your team's management. Transparent rates. Team Success layer for engagement health.
Senior data talent, vetted at the seven-plus year band
Seven-plus years professional engineering experience (average eight to twelve). Six-stage vetting including guild peer review. Production reliability instincts (for engineers) and decision-driving communication (for scientists) are part of the rubric.
Curated shortlist in 72 hours, not 72 days
Typically three to four senior builders matched to your data problem (pipeline reliability, warehouse optimization, ML modeling, applied analytics), your timezone, and your rate band. You interview. Working builder embedded by Week 2.
Per-builder rate stated on every Service Order
One rate per builder on the Service Order, no hidden tiers or success fees. Procurement-ready.
Three steps from scoping call to embedded builder.
Day 0: Scoping call
A 30-minute call to understand the work, the timezone, the rate band, and the engagement shape. No JD required.
Day 3: Curated shortlist
A curated shortlist (typically three to four senior builders) matched to your scope. You interview, you select.
Week 2: Builder embedded
Selected builder starts under your team's management. Team Success contact runs kickoff and stays close throughout.
Ready to scope an engagement?
Get a shortlistSenior data work covers four engagement archetypes.
Data engineering (pipelines, warehouses, transformations)
ETL/ELT pipelines from source to warehouse. Schema and data model design. Query optimization. Orchestration choices (Airflow, Prefect, dbt) made with reasons that hold up. The reliability work that determines whether downstream analysis is trustworthy.
Data science (analytics, modeling, decision support)
Cohort analysis, funnel analysis, revenue attribution, churn modeling. The analyses that translate into a decision the business actually makes. Uncertainty communicated honestly, not hidden behind false precision.
ML engineering (training, deployment, monitoring)
Predictive model design, training pipelines, deployment workflows, and the monitoring that catches drift before users do. Closer to engineering than to BI; the output is a deployed system, not a report.
Data product ownership
The senior owner who can hold the whole data system (pipelines, warehouse, models, dashboards) and make trade-offs across it. Often the highest-leverage hire when the data function has grown faster than its leadership.
Scoping call to working builder
Across recent senior data engagements, the median time from scoping to embedded builder is about two weeks. FTE searches average three to five months.
Monthly retainer savings vs hourly
Monthly retainers run 5 to 15 percent below the equivalent hourly cost at full utilization and lock in builder availability.
Conversion fee floor for direct hires
If a contractor converts to FTE, the fee is the greater of $20,000 or three months at the monthly rate plus 10 percent. Published, not embedded.
Procurement, security, and engagement governance built in.
MSA + per-builder Service Order
Standard A.Team commercial structure. Hourly or monthly rate per builder. No managing partner fee.
Net-15 invoicing
Standard MSA terms. One rate per builder on the Service Order, no hidden tiers or success fees.
Team Success layer
Named contact runs kickoff, checks engagement health, owns escalation. Not a managing partner; a Team Success contact.
Re-match if a builder isn't fitting
Team Success scopes a re-match against existing engagement context. No separate trial-period contract required.
IP assignment from day one
Standard work-for-hire language in every Service Order.
Global builder pool, regional matching
Senior builders across the US, Western Europe, and select nearshore markets. Matched to your timezone and rate band.
Skip the 3-to-5-month FTE search.
A.Team matches vetted senior data engineers and data scientists in 72 hours at transparent per-builder rates.
How to hire a data engineer
The decision framework for scoping a senior data engineering hire. Pipeline problem, scale, reliability requirements, and the common failure patterns.
How to hire a data scientist
The decision framework for scoping a senior data scientist hire. Decision-first scoping, the common mismatch between data scientist and data engineer hiring, and what the first 30 days should produce.
Common questions about hiring data engineers and data scientists through A.Team
If the data isn't flowing reliably, you need a data engineer first. If the data is in good shape and you need analysis that drives a specific business decision, you need a data scientist. If the work is building or fine-tuning ML models against your data, you need an ML engineer (often a data engineer with model-deployment experience). Many companies need a data engineer before they're ready to get value from a data scientist.
Senior US data engineer contractors typically run $125 to $175 per hour, with AI and ML data infrastructure specialists trending toward the upper end. Senior data scientists run $120 to $200+, with ML specialists (model training, fine-tuning, production ML systems) reaching $250 in the architect tier. Rates are builder-set and reflect seniority, location, and specialization. Monthly retainers run 5 to 15 percent below the equivalent hourly cost at full utilization.
Time from scoping call to shortlist: 72 hours. Time from scoping call to a working builder embedded in your team: roughly two weeks, depending on scope confirmation and your interview cadence.
On the engineering side: Airflow, dbt, Fivetran, Snowflake, Databricks are the most commonly placed (adjacent warehouses like BigQuery and Redshift are also represented). On the science side: Python and SQL are core, with Tableau and Looker the most common dashboarding surfaces (additional Python libraries and BI tools are represented in the network). Matching prioritizes the problem (pipeline reliability, warehouse design, predictive modeling) and seniority over specific tool fluency.
Yes. The conversion fee is the greater of $20,000 or three months at the monthly rate plus 10 percent. The structure is published in the master agreement.
Your team. A.Team's Team Success contact runs kickoff, checks in regularly throughout the engagement (cadence set with your team), and is the escalation path. There is no managing partner layer on standard team augmentation engagements.