Data engineers
The layer everything else is standing on. Analytics that nobody trusts and AI that hallucinates over your own documents usually trace back to the same unglamorous place.
- Typical seniority
- Mid-level through principal
- Core stack
- Spark, dbt, Airflow, Iceberg or Delta, Python, SQL
- Platforms
- AWS, Databricks, Snowflake, BigQuery
- Engagement shapes
- Single engineer, embedded pod, fixed-scope build
- Typical duration
- Three to twelve months
- Deliverable
- Pipelines with tests, contracts and documentation
Data engineers from CloudOps Network build and operate data platforms: ingestion pipelines, lakehouse architecture, transformation and modelling, data contracts, quality testing and governance. Engagements commonly precede or run alongside analytics and AI work that depends on the same foundation.
What data engineers do on our engagements
Ingestion pipelines
Batch and streaming ingestion that handles schema change, late data and the source system that goes down every Tuesday.
Lakehouse architecture
Table format, partitioning, compaction and the layout that decides whether your queries cost pennies or hundreds.
Transformation and modelling
Dimensional or wide, tested, documented, and structured so analysts stop rebuilding the same logic.
Data contracts
Explicit agreements between producers and consumers, so an upstream change breaks a build rather than a dashboard.
Quality and observability
Freshness, volume, distribution and referential checks, alerting before someone presents the wrong number.
Governance
Lineage, cataloguing, access control and retention that satisfy an auditor without stopping the analysts.
The order most AI projects wish they had used
A recurring pattern: a company starts an AI project, discovers three months in that the documents are inconsistent and the source data is untrusted, and pauses to do data work under deadline pressure.
Doing four to eight weeks of data engineering first is almost always cheaper, and it makes the AI work measurable when it starts.
Know what you have
An inventory of sources, owners and quality, before anything is built on top.
Fix the worst inputs
Usually a small number of sources cause most of the downstream problems.
Add contracts and tests
So the fix holds when the upstream team ships next month.
Then build
AI and analytics work on a foundation that can be measured and trusted.
A sample of the bench
Availability changes weekly. This is the shape of who is in the network, not a live list.
Illustrative profiles drawn from the current roster. Names and clients are withheld until a mutual NDA is in place; the shortlist you receive names real people with real references.
Questions about hiring data engineers
Do we need a data engineer before an AI engineer?
Often, yes. If your source documents or records are inconsistent, an AI engineer will spend their first weeks doing data work at a higher rate. We will say so during intake if that is what we see.
Which platforms do your data engineers work on?
AWS-native stacks, Databricks, Snowflake and BigQuery are all represented. Tell us your platform and we filter on demonstrated production experience with it.
Can they work with our analytics team?
Yes. Most engagements run alongside an existing analytics or BI function, and the handover of modelling standards is part of the work.
Do you cover streaming as well as batch?
Yes, though genuine streaming requirements are rarer than briefs suggest. A good engineer will ask what latency the business actually needs before committing you to the operational cost.
Tell us what you are building
Describe the work in a few lines. We come back with a shortlist of engineers who have done it before, their availability, and a rate range. No account, no platform fee, no obligation to hire.
Most briefs get a shortlist within three business days.