Hire Data Engineers
AI-native data engineers who own the pipeline from raw event to RAG corpus to evaluation set. Streaming, batch, vector, analytical — same engineer. From $115/hour, trial sprint included, monthly NTE ceiling.
5–10 day staffing · Top 3% pass · AI-native end-to-end
Sample profiles
Anonymized but accurate. Real engineers on our bench, real rates, real availability.
01
Location
Americas · ET
Background
ex-Snowflake field engineering, 9y; petabyte-scale ELT; vector + analytical workloads coexisting; data contracts
Stack
Snowflake · DBT · Airflow · Kafka · Postgres · pgvector · Python
$150/hr
02
Location
EMEA · 6 hr overlap with US ET
Background
ex-Datadog, 6y; streaming pipelines + observability; OLAP + OLTP boundaries; cost-aware architecture
Stack
BigQuery · DBT · Dagster · Kafka · ClickHouse · Python · Go
$130/hr
03
Location
Americas · PT
Background
ex-Stripe Data Platform, 7y; high-cardinality analytics; embeddings infrastructure; data quality at scale
Stack
BigQuery · Snowflake · DBT · Airflow · Spark · pgvector · Python
$130/hr
Vetting
01
Live problem-solving on real data tasks — modeling, performance tuning, schema design. Not generic LeetCode.
02
Candidate designs an end-to-end pipeline for a realistic ELT + AI workload with one of our principals.
03
Past managers and clients confirm shipped production work, not just titles.
04
Real, scoped work on our team before the engineer faces a customer.
What "AI-native data engineer" means
Most AI features fail at the data layer, not the model layer. A good RAG corpus, a clean ground-truth eval set, a fast embeddings index, and observability across both vector and analytical workloads — that's the unglamorous infrastructure that decides whether your agent answers correctly in production.
Our data engineers own all of it. Pipelines that ingest customer data, normalize, redact PII, embed, index, version. Eval datasets that catch regressions. Cost dashboards that flag a runaway embedding job before it's a $10k surprise.
Other engineering roles
Adjacent services
Frequently asked
Three pricing paths. AISD staff augmentation (senior, AI-native): $95–175/hour depending on seniority and engagement length. Marketplace contract (Toptal, Turing, Upwork): wide range — $40–200/hour with high variance in quality. Full-time hire (US): total comp typically $200k–$450k for a senior AI engineer. The hidden cost in hires is recruiting (3–6 months) and ramp (2–3 months). Staff augmentation pays off when you need impact in <90 days.
Every AISD engineer passes four gates. Technical screen — live problem-solving on AI engineering tasks (not generic LeetCode). System design — they design a production AI system end-to-end with one of our principals. Reference check — past clients confirm shipped production work. Paid trial sprint — a real, scoped piece of work with our team before the engineer faces a customer. Roughly 3% of applicants pass all four.
Three differences. First, every AISD engineer is senior — minimum 5 years building production software, with shipped AI features. Second, we publish hourly engagement bands and project ranges so you know roughly what an engagement costs before the first call. Third, we take fewer concurrent projects so a partner stays close to delivery.
All three. Fixed-price for AI MVPs and agent builds where scope is well-defined after a discovery sprint. Time-and-materials for staff augmentation, billed monthly with a not-to-exceed ceiling. Retainer for ongoing optimization, eval-harness operations, and managed AI services — flat monthly fee for a defined scope of capacity.
Success metrics are defined in writing during scoping and reviewed monthly. Project engagements measure: feature shipped on date, eval-harness pass rate, target business metric (e.g. 'auto-resolve rate ≥35% on customer-support tickets'). Staff augmentation engagements measure: PR throughput, code-review acceptance, and customer-side satisfaction. We do not measure success in hours billed, lines of code, or generic velocity points.
Working prototype: 2 weeks. Production-grade agent (with eval harness, guardrails, observability, and a runbook): 6–10 weeks. The prototype-to-production gap is where most projects fail — the prototype handles the happy path; production has to handle the long tail.
Most AI MVPs at AISD ship a usable version in 4–8 weeks. Week 1 is a discovery sprint. Weeks 2–6 are the build, with weekly demos and a working version by week 4. Weeks 7–8 harden, document, and hand off.
Consulting produces decisions and plans; development produces working software. AISD does both, often in sequence: a consulting engagement scopes the architecture and roadmap, then a build engagement implements it. Consulting alone is right when you're early in the AI journey, evaluating vendors, or auditing existing work. Build alone is right when scope is already clear. Most AISD customers do a 2-week paid discovery sprint first — that's a consulting engagement that produces a fixed-price build proposal.