Data
Master the data. End to end. In one thread.
Connect it, profile it, fix it, transform it, trace it, publish it, and keep it true when the source moves. The work that used to queue behind engineering happens in a conversation, previewed before every write.
“keep this dataset live”
Live sync
Every fifteen minutes, hourly, daily, on a schedule, or real-time change capture from the major relational databases. When a sync lands, it cascades to the pipelines, dashboards and APIs built on it. A spend cap per period. It warns you before a sync heads for suspension.
“profile this”
Data profiling
Built for four million rows by three hundred columns. The expensive checks run inside your database over the whole table, never a full download. Anything estimated is labelled as estimated. A quality score, null and distinct counts, inferred types, in one answer.
“change step 3 to a left join”
Data transformations
A visual flow with code steps, refined in conversation with versioned undo. Saved work becomes a recipe the whole team reuses. Plans push down to the source and run at engine scale, on a schedule or whenever the data changes.
“what breaks if I drop customer_id?”
Data lineage & impact
Upstream chain, downstream blast radius to the column: every report, feed and dashboard that would move, before it costs someone a weekend. Find a column by meaning, not by name.
“order_id must be unique”
Quality & validation
Assert your own rules: uniqueness, keys, ranges, freshness, patterns, cross-field. Cleanup detects, ranks the fixes, applies only the safe ones on a reversible copy, and lists the risky ones for your call. A standing drift watch polls the source on a cadence.
“find the revenue tables”
Catalog & glossary
Search across names, descriptions, tags and meaning. A record per dataset that the intelligence consults before it answers. What you settle in conversation is written back onto that record, with the previous values named.
“whenever the feed changes”
Scheduling & triggers
Standing orders with their own spend cap and quiet hours. Automate on change: a pipeline, a dashboard or an API refreshes whenever its source dataset moves.
“restore last Tuesday’s version”
Versioning & restore
Version history with restore for dashboards, datasets and fixes. A restore is itself on the record, so the state you left is never lost.
“push the cleaned rows to Snowflake”
Deliver back out
Write a dataset out to a write-capable warehouse or database. Your role’s column masks apply to what is written, and the delivery can be swapped back. Generate synthetic rows from a dataset’s statistics when you need test data that keeps its secrets.
- PostgreSQL
- MySQL
- SQL Server
- Oracle
- Snowflake
- MongoDB
- Redis
- SQLite
- Amazon S3
- Google Cloud Storage
- Azure Blob
- SharePoint / OneDrive
- APIs & SaaS
- Files
- Edge Agent, outbound only