Fractional Data Engineering
Hands-on engineering support for pipelines, integrations, dbt models, automation, reporting, and production data workflows.
Best when the work needs to move, but hiring is slow or the team needs extra senior capacity.
Practical architecture, not theater.
The consulting offer is built around diagnosing the real constraint, making tradeoffs visible, and leaving the team with systems they can operate. The exact scope depends on the current state, but the working style stays direct: assess, prioritize, build, document, and hand off through The Gambill Data Platform Risk Review.
- Pipeline, integration, and reporting automation implementation
- SQL, Python, PySpark, orchestration, dbt, Snowflake, Databricks, Azure, AWS, and cloud data engineering
- Code review, documentation, and operational handoff
- Reliable delivery practices for recurring data work, medallion architecture, and pipeline observability
Use this offer when the symptoms are already visible.
Important pipeline, integration, or reporting work is waiting on capacity
Manual reporting processes need to become reliable automated workflows
The team needs senior SQL, Python, PySpark, Snowflake, Databricks, Azure, or AWS support
Existing pipelines work, but documentation, monitoring, testing, and recovery are weak
You need implementation help that includes handoff, not just code delivery
A practical first conversation, not a vague discovery script.
The first call is meant to clarify the current state, identify the useful next move, and decide whether this offer is the right fit. You do not need a perfect brief before scheduling.
- Which workflows need to move first
- What systems, sources, schedules, and reporting requirements are involved
- Where reliability, quality, or handoff risk exists
- Whether the work needs build support, review support, or temporary senior capacity
Common questions.
Can you implement, not just advise?
Yes. This offer is for hands-on engineering support across pipelines, integrations, automation, modeling, reporting, and production workflows.
Can you work in our existing stack?
Usually, yes. Experience includes SQL Server, Snowflake, Databricks, Azure, AWS, Informatica, Python, PySpark, Power BI, and Tableau-adjacent workflows.
Will our team be able to maintain the work afterward?
That is the goal. Documentation, handoff, and operational clarity are part of the engagement.
Want a clearer read on the current state?
Book a strategy session and we will separate symptoms from causes, then identify the next useful move.