Hire a Data Engineer
Builds the reliable data platform your analytics and AI actually run on.
A dedicated data engineer who owns ingestion, ELT, and the warehouse or lakehouse underneath your business: streaming, orchestration, data quality, and governance, with a senior lead reviewing every pipeline that touches production data.
30-minute fit call with an engineering lead. No hard sell, no deposit, and no change to the published rates.
What they own: the data platform your team can actually rely on.
Ingestion & ELT pipelines
Connectors and ELT jobs that pull from your apps, databases, and third-party APIs into one warehouse, then transform with dbt into clean, versioned, documented models.
Lakehouse & warehouse modeling
A layered warehouse or lakehouse (staging, core, marts) with clear grain, tests, and ownership, so every table has one meaning and one team accountable for it.
Real-time streaming (Kafka / Flink)
Event pipelines on Kafka or Flink for data that cannot wait for a nightly batch: live metrics, fraud signals, and operational feeds with exactly-once handling.
Data quality & observability
Automated tests, freshness and volume checks, and alerts that catch bad or late data before it reaches a dashboard or a model, not after a stakeholder does.
Orchestration & scheduling
Airflow or Dagster DAGs that run jobs in the right order, retry on failure, backfill history, and give you a clear view of what ran, when, and why it broke.
Governance, lineage & cost control
Column-level lineage, access controls, and a catalog so people find and trust data, plus warehouse tuning that keeps compute and storage spend in check.
When to hire a Data Engineer, and what week one proves.
- ●Your data is scattered across tools and no one fully trusts it
- ●Analysts wait days for data that should be ready every morning
- ●Pipelines break silently and you find out from a wrong dashboard
- ●There is no single source of truth — every team has its own numbers
- ●Your data scientist spends most of their time cleaning data, not modelling
- 01Audited your existing pipelines, warehouse spend, and failure points
- 02Stood up a clean, tested dbt staging-to-mart slice on a real source
- 03Added automated freshness, volume, and schema-drift assertions
- 04Cut the most expensive runaway warehouse queries
- ✓A trusted single source of truth every team reads from
- ✓Fresh, tested, documented data that lands on schedule
- ✓Pipelines that retry, self-heal, and alert before users notice
- ✓Lower warehouse spend from tuned models and queries
- ✓Analysts and models unblocked, working from clean data
Where a Data Engineer fits, and where they don't.
From role brief to production evidence.
You are not buying a resume. You are choosing a specific engineer, then testing the match on committed work before making a longer decision.
- 01
Map the real gap
A 30-minute call with an engineering lead defines what the Data Engineer must own, the stack they inherit, and the evidence that will count as a successful trial.
- 02
Review matched profiles
Within 48 hours, you receive 2–3 role-matched profiles. Review work history, technical evidence, certifications, and availability before choosing whom to interview.
- 03
Interview against the work
We help turn your current failure cases into practical interview scenarios. You choose the engineer; no profile moves forward without your approval.
- 04
Prove fit in one paid week
The engineer works in your repo or approved data environment. You judge real output, communication, and technical decisions before any month-to-month continuation.
No deposit, no unpaid test project, and no long-term contract required. Trial work is paid at the published rate and belongs to you.
Pick the level, keep the senior oversight.
- ✓Builds and maintains scoped ingestion jobs and dbt models under a senior
- ✓Writes tests, freshness checks, and documentation on every model
- ✓Every pipeline change reviewed before it reaches production data
- ✓Designs the warehouse or lakehouse architecture and modeling standards
- ✓Owns streaming, orchestration, governance, lineage, and warehouse cost
- ✓Mentors your team, reviews work, and sets the data quality bar
Dedicated engineers are billed monthly; Time & Material is billed hourly on tracked actuals. The paid one-week trial applies to every dedicated hire.
Most teams add a second seat once the first proves out.
What teams ask before they shortlist.
Comparing multiple engineering seats? Browse all 9 dedicated AI & data roles or book a 30-minute fit call to map the right boundary.
Batch or streaming — which do we need?
Most teams need solid batch first: reliable nightly or hourly loads into a clean warehouse cover the large majority of analytics and reporting. Streaming is worth the extra complexity only when a real decision cannot wait — live fraud checks, operational dashboards, or user-facing features. On the call we look at your actual latency needs so you do not pay for streaming you will not use.
Which warehouse or lakehouse should we pick?
It depends on your data volume, team skills, and cloud. Snowflake and BigQuery are strong managed warehouses for most analytics; Databricks and a lakehouse fit heavier data science and large unstructured data. We assess what you already run and what your workloads need, then recommend one rather than pushing a favorite.
How long until a first reliable pipeline?
For a scoped source landing in a clean, tested set of tables, a first pipeline is usually running within the paid one-week trial. Broader platform work — many sources, streaming, or a full remodel — takes longer, but we sequence it so you get a trustworthy first slice early instead of waiting months for everything at once.
How do you handle data quality and testing?
Every model ships with tests: not-null, uniqueness, accepted values, and referential checks, plus freshness and row-count monitors on the pipeline. Bad or late data raises an alert and, where possible, is quarantined before it reaches a dashboard or a model, so problems are caught at the source, not by a confused stakeholder.
Who owns governance and lineage?
A senior data engineer sets it up and your team runs it: column-level lineage so you can trace any number back to its source, a catalog with owners and definitions, and access controls that fit your compliance needs. The goal is data people can find, trust, and use without asking around.
What about IP, replacement, and timezone?
NDA and IP assignment are signed before onboarding and everything produced belongs to you, including trial work. If the fit is wrong, we provide a free replacement with a documented handover. Engagements are planned with overlap for your working hours, in English, every day.
Role-specific hiring & engineering playbooks for Data Engineers.
How to Hire an AI Infrastructure Engineer: Gateways, vLLM, GPUs, and Scale
How to Hire a DevOps Engineer for AI Workloads
Self-Hosted vLLM vs. Managed Model APIs: Unit Economics and Failover
How to Build a Multi-Model AI Gateway with Automatic Fallbacks
7 Inference Cost Levers That Turn Negative AI Margins Positive
LLM Model Routing: Send 80% of Traffic to the Cheap Model
Prefer to train your team first? Get the engineering field manuals.
Interview a Data Engineer this week.
Bring your stack, your failure cases, and your constraints. We'll send 2–3 vetted profiles within 48 hours, then use the paid one-week trial to prove fit in your environment.
30-minute fit call · No hard sell, no deposit, and no unpaid test project · NDA before onboarding · Free replacement · No lock-in