We help you hire data engineers who think in systems, document their work, and design for scale.
The engineers we place build for stability from the start. That way, you don’t end up solving the same issue twice.
Engineers who build with maintenance in mind.
Structured for reporting, scaling, and change.
You’ll know what broke, when, and why it happened.
Collaborate smoothly across data, ops, and QA.
We see these red flags early and screen them out before they reach your shortlist.
We stand behind every hire with a 90-day guarantee
STACK IT data engineers are vetted for query skills, stakeholder alignment, and the ability to deliver when inputs are unclear.
Our client struggled with inconsistencies in reporting across regions. The engineer we placed rebuilt the SQL layers and dashboards, resulting in a 75% reduction in report turnaround time.
Equip your team with full-time engineers who build stable, documented pipelines.
STACK IT’s evaluation framework reflects the real conditions where data engineering breaks down and where the right hire prevents it.
Strong SQL is about understanding what the business needs and building models that align with them. We seek engineers who effectively design solutions for long-term use by analytics teams.
Data engineers are evaluated on how they structure and schedule pipelines. That includes DAG transparency, handling failure cases, and building workflows that can withstand high loads.
We ask how they handled real failures, caught data drift, and made sure the same problem didn’t happen twice. Data engineers who’ve carried that responsibility tend to build differently.
We closely examine how candidates document logic and assist other engineers in troubleshooting to prevent analysts from using black-box systems they don’t understand.
We evaluate how engineers operate with teams. Strong candidates adjust their workflow without introducing new issues and take ownership of the downstream impact.
STACK IT data engineers have rebuilt reporting pipelines, automated slow workflows, and resolved logic gaps that held back teams across product, finance, and ops.
A SaaS company’s ARR numbers were inconsistent and lacked trustworthiness.
Our engineer traced it to redundant dbt models and undocumented logic. They rebuilt the reporting layer and helped identify a $280k forecast gap before quarter-end.
Data engineers at a national retailer were manually cleaning data for every weekly report.
We placed a contractor who rebuilt the dbt layer, added transformation tests, and introduced audit-ready outputs, cutting prep time from 6 hours to under 45 minutes.
A health tech company faced significant delays in reporting across finance, product, and operations.
Our engineer restructured the Airflow schedule, reworked the transformation logic, and added schema alerts, resulting in a reduction of over 70% in reporting delays.
We place data engineers with hands-on experience across pipeline types, tooling stacks, and production workflows. The table below highlights the specializations we support most often.
| Specialization | Key Skills / Tools | Use Cases |
|---|---|---|
| Batch Pipelines | SQL, dbt, Airflow, orchestration tools | Reporting layers, metric reliability |
| Streaming | Kafka, Spark, schema evolution handling | Real-time feeds, fraud detection, alerts |
| Infra-as-Data | CI/CD, Terraform, Docker, container orchestration | Reproducibility, deployment, versioning |
| Observability | Great Expectations, Monte Carlo, custom alerting | Trust layers, pipeline transparency |
| Governance & Lineage | Collibra, Alation, dbt docs, lineage graphs | Regulatory compliance, audit prep, PII and access control |
We’ll help you choose the right model, whether you need short-term pipeline rebuilds or long-term data ownership.
Most data engineering issues don’t show up until something breaks. We vet for the things that prevent silent failures.
Data quality incidents are one of the most expensive forms of rework in modern data stacks.
According to Monte Carlo’s 2022 survey, Data Engineers spend 40% of their time resolving bad data. Most incidents take hours to detect and nearly a full day to fix, draining focus and slowing down delivery.
2-3
weeks
Average time
to hire full-time
That’s why our clients stay and why our hires do too.
A fintech client was seeing pipeline issues during reconciliation. Our data engineer rebuilt the DAG logic and added validation steps, cutting month-end reporting errors by 60%.
Yes. And we go deeper by reviewing how those tools shaped performance, trust, and teams in past roles.
Absolutely. We specialize in contract placements for high-impact rebuilds or critical handoff prep.
We use repo prompts and scenario reviews to evaluate what candidates actually write down, how they structure logic, and how they support others.
For contract data engineers: 1–2 weeks. For full-time: 2–4 weeks depending on complexity and region.
Data Engineers build and maintain infrastructure: pipelines, orchestration, transformation layers. Analytics Engineers sit closer to business logic, dashboards, and stakeholder delivery.
When data breaks, decisions are delayed. We place engineers who build pipelines that your team can trust, without cleanup.
That’s our STACK.