The layer everything
downstream depends on.
Lakes, lakehouses and warehouses — architecture, build and migration, then the reporting on top.
What Attri’s data engineering practice covers
Analytics programmes and agent projects tend to stall in the same place: nobody can say whether the numbers are right. Attri builds the layer underneath that answers it.
Platform and modelling
Architecture on Snowflake, Databricks or Fabric, with medallion and dimensional models and pipelines that are testable rather than merely scheduled.
Migration
Off legacy warehouses and older ETL tooling, usually while the existing reports keep running.
Governance and BI
Lineage and quality validation so a number can be defended, then the reporting layer in Power BI and Tableau.
What we build with
We build in the estate you already run, including the parts you inherited, rather than moving you onto what we would have picked.
- Snowflake
- Databricks
- Microsoft Fabric
- Delta Lake
- Azure Data Factory
- Synapse
- Informatica
- T-SQL
- PySpark
- Power BI
- Tableau
Where we put agents in a data migration
Agents take on the work that scales with volume rather than judgement. What they produce goes to the engineer who owns that area before any of it ships.
Legacy pipeline reading
Agents parse Informatica mappings and stored procedures to recover transformation logic that exists nowhere else — the part of a warehouse migration with no fallback.
Model drafting
Dimensional and medallion models proposed from profiled source data, then argued over by engineers rather than typed by them.
Quality rules
Validation and anomaly rules generated per table from observed distributions, so checks cover the estate rather than the tables someone remembered.
Put agents on your estate.
See how we scope, govern and deploy them — with the guardrails regulated teams need.
Tell us what you are running.
A system to build, or one you have inherited. Either way the first conversation is the same: what exists, what is breaking, and what has to stay live while it changes.