Data Engineering your reporting can rely on
Trusted pipelines and models so reporting, forecasting and AI run on the same numbers.
- One
- Definition per metric
- Tested
- Every pipeline
- Shared
- Analytics and AI layer
What this solves.
When two teams present different numbers for the same month, the problem is not the dashboard. It is the absence of modelled, tested, owned data between the source systems and the report.
One definition per metric
Modelled once, reused everywhere.
Pipelines that alert
Failures surface before month-end, not during it.
AI-ready by default
The same layer feeds retrieval and analytics.
What leaders tell us is not working.
Numbers disagree
Each team extracts and transforms its own way, so reconciliation eats the reporting cycle.
Manual extracts
Reports depend on someone exporting a file on a schedule they may forget.
No lineage
Nobody can explain where a figure came from or what changed when it moved.
How we deliver it.
Model the core
Agree entities, grain and metric definitions with the people who use them.
- Source assessment
- Dimensional models
- Metric dictionary
Build the pipelines
Ingestion, transformation and tests as code, with alerting.
- Batch and incremental loads
- Data quality tests
- Lineage and alerting
Serve consumers
Reporting, forecasting and retrieval reading from the same layer.
- BI models
- Warehouse-backed APIs
- Vector and search feeds
What we build and run with.
Storage
- PostgreSQL
- BigQuery
- Snowflake
- S3 / Blob
Pipelines
- dbt
- Airflow
- Change data capture
- Python
Consumption
- Power BI
- Metabase
- pgvector
- APIs
What good looks like.
- One
- Definition per metric
Agreed, documented and reused.
- Tested
- Every pipeline
Quality checks fail loudly and early.
- Shared
- Analytics and AI layer
The same trusted data serves both.
Who delivers this work.
Where this lands most often.
Where we deliver this.
- India
- KSA
- UAE
- Oman
- Qatar
- USA
- UK
Proof from comparable engagements.
How we think about this work.
Take the detail with you.
Lucidspire company profile
Capabilities, delivery model and engagement options in one document.
Questions we are asked first.
Ready to move on this?
Talk to us
Share your data engineering brief
Tell us the outcome you need. We will come back with the approach, the shape of the team and the timeline.
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