Hire an AI data engineer for the foundation everything else depends on.
The data foundation every AI initiative eventually depends on.
Most AI projects stall on data before they stall on models — inconsistent sources, no quality monitoring, nothing retrievable at the volume production requires. Our data engineers build the ingestion pipelines and warehouse/lakehouse infrastructure that make everything downstream possible.
- Seniority
- 5+ years data engineering
- Background
- Ingestion pipelines, warehouse/lakehouse design, data quality monitoring, dbt/Airflow-style orchestration
What they'll actually do.
Not a job description — the work this person owns from their first sprint, inside your codebase and your process.
Work this bench has shipped.
Concordia Colleges — 150-Branch SIS Platform
A complete Student Information System for a network of 150+ college branches: revenue and royalty management, fees, LMS, student and teacher operations, payroll, and accounts — unified on one platform.
Haystack — AI Data Intelligence Platform
A data-driven intelligence platform: one AI search layer across documents, data lakes, and repositories, with real-time indexing, AI-driven correlation, and workflow automation — built to hold up under heavy data loads.
Beyond AIO — AI Business Management & Analytics Platform
An all-in-one AI-powered business platform: predictive analytics, competitor analysis, CRM, HRM, financial management, task management, and cross-channel marketing with real-time campaign optimization.
Disclosure — founder-owned venture
Tell us the role, the stack, and when you need them started.
Every figure has a name behind it.
No rounded-up vanity metrics — each number below is tied to a specific, named engagement this bench shipped.
From intro call to embedded.
Matched by engineers who've built pipelines that survive real data, not a clean sample dataset.
Intro call
Confirm the role, stack, and team fit in one call.
Match
We propose 1–2 engineers from the bench who fit the work, not a generic pool.
Trial week
A real first week of work before any longer commitment.
Embed
Full participation in your standups, sprints, and tooling.
If the fit isn't right, we replace them inside two weeks — no argument, no fee for the swap. You'd rather we caught it early, and so would we.
Beyond Technologies ensured a streamlined workflow with consistent project updates throughout — the platform now runs our entire network.
Asked on every first call.
An AI data engineer builds the same ingestion, warehouse and quality infrastructure a general data engineer builds, and then the layer AI actually needs on top of it: data made retrievable at production volume through vector search or a feature store, and quality monitoring strict enough that a model is never grounded on a pipeline that broke quietly last Tuesday.
Yes, and that is the default. A staff-augmentation data engineer works inside your codebase, your warehouse and your standups, not in a separate vendor environment.
An AI data engineer typically starts within one to two weeks of the intro call. We match from an existing bench rather than opening a cold hiring pipeline, so the limiting factor is usually your onboarding and access paperwork rather than our recruiting.
Every data engineer placement carries a two-week replacement guarantee, with no argument and no fee for the swap. We would rather replace someone in week two than have you carry a bad fit for a quarter.
No. A data engineer is engaged day-rate or monthly, month-to-month after a four-week minimum. No tiers, no bundled products, no notice period designed to trap you.
You do, from the first commit. A data engineer works in your repositories and your warehouse, and all work product assigns to you — pipeline and orchestration code, warehouse and model definitions, quality checks, and documentation. The IP assignment is signed before anyone is given repository access, not negotiated at handover.
One call. Then a name, not a pipeline.
- 30 minutes with a senior engineer, not a recruiter
- Free and no-obligation — bring the role and the stack
- You leave knowing who we'd match and how fast they can start
- We reply within one business day
Anthropic-certified engineering capacity for Claude-native products and agents.
Senior LLM application engineering, without the vendor lock-in.
Agent and workflow-automation engineering, embedded in your team.
Full-stack web and mobile engineering, AI-accelerated, senior-owned.
Fractional technical leadership for teams that need direction, not headcount.
Fractional AI leadership — roadmap, governance, and vendor decisions.
Retrieval pipeline engineering — the discipline behind every trustworthy RAG system.
Deployment, monitoring, and cost control for production AI systems.
Systematic prompt and context design, tested against real outcomes.
Product management for AI features and roadmaps, grounded in what's actually feasible.
Let's put AI to work in your business.
A 30-minute call. You bring the workflow or the roadmap — we'll tell you what's feasible, what it costs, and what we'd build first.