Database Management
The database behind growth, kept healthy.
The operational databases your growth stack depends on — customer records, subscriptions, events, product catalogs — kept fast, clean and backed up. Schema design and migrations, indexing and query tuning, deduplication and hygiene rules, replication so analytics never contends with production, and monitoring that pages someone before a slow query becomes a checkout that times out.
Database Management keeps the operational databases your growth stack depends on fast, clean and backed up. That means schema review with reversible migrations, index and query tuning against real traffic, deduplication at write time, read replicas so analytics never touches production, tested restores, and monitoring for slow queries, lag and storage.
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The tension
The database was designed for the first version of the product and has been patched ever since. Duplicate customers, columns whose meaning nobody remembers, a nightly job that has been failing quietly for weeks, and analytics queries running against production because there is nowhere else to run them.
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The shift
A schema that matches what the business is now, migrations that are reviewed and reversible, indexes chosen against real query patterns, and deduplication and validation applied at write time rather than apologized for later. Read replicas take the analytical load off the path your customers are on, and monitoring covers slow queries, replication lag, storage and backups.
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The outcome
Records that resolve to one customer, queries that return before anyone reaches for a coffee, and restores that have actually been tested. The layer under every growth number stops being the thing everyone is quietly afraid of.
What you get
Concrete, not fluff.
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Schema review, with reversible migrations
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Index and query tuning against real traffic
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Deduplication and validation applied at write time
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Read replicas so analytics never touches production
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Backups, retention and tested restores
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Monitoring for slow queries, lag and storage
How it works
Brief in, live campaign out.
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Audit schema, queries and hygiene
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Fix what is unsafe first
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Model and migrate
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Tune indexes and queries
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Monitor, back up and verify restores
It loops — every cycle feeds the next.
The proof
A restore you have never tested is a hope.
Slow-query counts, replication lag and backup verification tracked together, with each fix visible as the moment the line drops.
A marketplace had three records for a meaningful share of its repeat buyers, which made retention look worse than it was and sent the same person two competing offers. Write-time matching plus a one-off merge collapsed the duplicates and moved measured repeat rate by several points — none of it new revenue, all of it revenue that had been there and was miscounted. Directional, not a named client.
FAQ
Frequently asked questions
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Yes. The warehouse is where analysis happens; these are the live databases your product and your CRM run on. They have different failure modes — the warehouse gets slow and expensive, an operational database gets slow and takes checkout with it.
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Only with agreed access, a change window and a rollback path, and read-only until there is a reason not to be. Most of the early work — auditing, query analysis, replica setup — needs no write access at all.
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It is the usual situation. The work is scoped so it can be run by a team without one, and the parts that genuinely need judgment stay with us until you would rather they did not.
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Nothing irreversible without your sign-off. Matching rules are agreed and dry-run first, merges are logged, and the pre-merge state is retained — because a bad merge is far harder to undo than a duplicate is to live with.
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Access control, retention windows, audit logging and deletion propagation, yes. We are not your legal advisers — the policy is yours — but the database side of honoring it is ours.