Review
Map sources, access, sensitivity, current reports, and reliability risk.
Engagement models
How engagements start
Pricing is quoted after architecture review because source count, data sensitivity, access model, reliability needs, dashboard scope, governance evidence, and handover expectations change the work. The models below explain how we scope the right starting path.
Buying path
Review, then choose the right operating model.
Sprint
Build the accepted pipeline, model, dashboard, workflow, or proof artifact.
Fractional retainer
Operate roadmap, reliability, changes, incidents, and handover evidence.
Every path starts with source review and ends with handover evidence.
Quote logic
What has to be understood before a serious quote.
A data engineering quote is not just a count of dashboards or connectors. The same dashboard can be simple or high-risk depending on source ownership, PII boundaries, freshness requirements, warehouse shape, model complexity, review controls, and who must accept the final number.
DataDost quotes after reviewing the current stack because the real work is source-to-decision reliability: access, ingestion, models, tests, lineage, dashboard acceptance, runbooks, and handover evidence.
Data Stack Review
1-2 weeksWhen leadership needs a source and metric risk map before committing to a build.
- Includes
- Architecture review, source inventory, metric-risk review, and prioritized pilot plan.
- Output
- Source map and pilot recommendation
Build Sprint
4-8 weeksWhen one accepted pipeline, model layer, dashboard, or workflow must ship.
- Includes
- Ingestion, warehouse/model layer, dashboard, quality checks, UAT, and handover packet.
- Output
- Working release and runbook
Fractional Data Team
MonthlyWhen the company needs senior data engineering capacity before hiring internally.
- Includes
- Roadmap ownership, delivery governance, architecture decisions, and implementation support.
- Output
- Operating cadence and delivery backlog
Reliability Retainer
MonthlyWhen an existing data system needs monitoring, fixes, and controlled change handling.
- Includes
- Incident review, model/dashboard changes, quality-control follow-through, and stakeholder reporting.
- Output
- Reliability evidence and change log
Model details
How each engagement should be used
Data Stack Review
A bounded architecture review that delivers a source inventory, metric-risk register, and a recommended first pilot. Choose it when the team needs a defensible map before buying implementation.
Build Sprint
A scoped six-to-eight week sprint that ships one trusted pipeline, model layer, dashboard, or workflow with tests, acceptance notes, and a handover runbook.
Fractional Data Team
Senior data engineering capacity for roadmap slices, architecture decisions, delivery governance, and stakeholder cadence before hiring a permanent team.
Reliability Retainer
Ongoing monitoring, controlled fixes, incident review, and quality follow-through for production data work, with reliability evidence and a change log every month.
Next step
Start with the architecture review, then choose the right model.
Bring the messy version of your current sources, reports, dashboards, spreadsheets, and operational questions. We will map the first useful scope before proposing a sprint, fractional model, or retainer.
Inputs that shape quote
Source systems and access model
PII, retention, and role boundaries
Refresh, uptime, and incident expectations
Warehouse, dbt, dashboard, and workflow scope
Evidence, UAT, and handover requirements