Data Platform Audit
Find the platform issues that matter most and what to fix first.
5–10 working days
- Platform assessment
- Prioritized findings
- Architecture recommendations
- 30/90-day roadmap and readout
Data engineering consulting for systems your team can run.
Northgrain designs, builds and improves the pipelines, warehouse models and orchestration behind reporting and data products. The work happens in your repository, with tests, decisions and handoff kept visible.
We know something is wrong, but not what to fix first
The platform is unreliable, costly, or difficult to maintain.
Start with a Data Platform AuditWe have a defined problem that needs to be delivered
The scope is known; the team needs specialist delivery capacity.
Choose a Data Engineering SprintWe need consistent engineering capacity
The backlog is growing and the platform needs consistent ownership.
Explore Embedded Data Engineering
Choose the right delivery shape.
Data Engineering Sprint
Deliver one agreed engineering outcome in a focused time-box.
3–6 weeks
- Agreed scope and acceptance criteria
- Implementation
- Validation and testing
- Documentation, handoff, and knowledge transfer
Embedded Data Engineering
Add recurring engineering capacity and technical ownership.
Typically 3+ months
- Engineering capacity and backlog delivery
- Platform improvements
- Ongoing technical ownership
- Reliability support and regular reviews
Compare scope, duration and ownership.
Data Platform Audit
Data Engineering Sprint
Embedded Data Engineering
Primary need
Data Platform AuditUnclear platform priorities
Data Engineering SprintA defined delivery gap
Embedded Data EngineeringAn ongoing delivery backlog
Duration
Data Platform Audit5–10 working days
Data Engineering Sprint3–6 weeks
Embedded Data EngineeringTypically 3+ months
Outcome
Data Platform AuditFind the platform issues that matter most and what to fix first.
Data Engineering SprintDeliver one agreed engineering outcome in a focused time-box.
Embedded Data EngineeringAdd recurring engineering capacity and technical ownership.
Your involvement
Data Platform AuditContext and readout
Data Engineering SprintScope and validation
Embedded Data EngineeringRegular planning and reviews
Data engineering consulting services.
Data pipeline consulting
Design and implement ingestion paths that make ownership, retries and failure handling explicit.
Data warehouse engineering
Build Snowflake and BigQuery layers with documented grain, workload boundaries and reporting interfaces.
dbt and analytics engineering
Make models, tests, contracts and project conventions easier to review and change.
Airflow and Dagster orchestration
Make dependencies, schedules, retries and failure handling easier to operate.
Data quality and pipeline testing
Catch broken data before it reaches a dashboard or product decision.
Embedded data engineering capacity
Add recurring senior delivery inside the repositories and planning cadence your team already uses.
Documentation and handoff
Leave decisions, ownership, and working context with the team.
Built for established modern data environments.
Improve the platform you already use. Hands-on delivery in production stacks, not advisory-only.
Specialist consulting for a defined platform.
dbt consulting
Audit, refactor or implement the transformation project your team already owns.
Explore this serviceSnowflake consulting
Work on account cost, query performance, architecture and dbt inside Snowflake.
Explore this serviceData warehouse consulting
Warehouse-agnostic architecture, models, migrations and reporting layers for the platform your team uses.
Explore this serviceAnswered before the first call.
Not sure which engagement fits?
Tell us what is happening with your platform and we will help define the most practical next step.