Founder’s note
I built Data Jockey after watching good teams slow down for the wrong reasons: definitions in slides, fixes buried in tickets, and lineage known only to a few people. Important context went missing, incidents dragged on, and simple changes felt risky.
Our answer is a single, practical workflow—Metadata → Profile → Quality → Lineage. Put facts and guardrails in one place so stewards, product owners, and engineers see the same truth and move together.
- Workflow over wallpaper: profile → generate rules → monitor → communicate.
- Export-first: CSV / JSON / DDL so your work is portable.
- Evidence, not opinion: tests, incidents, lineage, and owners side-by-side.
We make enterprise data understandable, trustworthy, and fast to change
Data Jockey turns scattered schemas, definitions, and checks into one workflow: Metadata → Profile → Quality → Lineage.
Vision
A world where teams can ship decisions with confidence because lineage, definitions, and quality are visible, shared, and current.
Mission
Turn the messy reality of enterprise data into shared maps, crisp definitions, and enforceable guardrails—built into everyday work, not hidden in docs.
What we do
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M
Metadata
Catalog datasets, owners, domains, tags and generate target-specific DDLs (Postgres, Snowflake, BigQuery, SQL Server, and more).
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P
Profile
Infer types, ranges, quartiles, top-k categories, valid value sets, and null percentages—turn insights into rules in one click.
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Q
Quality
Create and monitor checks (range, regex, enum, NOT NULL, PK/FK candidates), track incidents and SLAs, export evidence.
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L
Lineage
See upstream/downstream impact by system and domain. Communicate blast radius, ownership, and risk with executive-friendly maps.
Our story
Data Jockey began as an internal workbench to tame schema sprawl and speed up data change. We bundled the essentials—metadata, profiling, quality, and lineage—into one opinionated flow so engineers, analysts, and leaders can speak the same language and move faster.
The reality data teams face
- Silos everywhere: warehouse, lake, BI, notebooks—context lives in ten places.
- Broken handoffs: requirements in slides, fixes in tickets, decisions lost in chat.
- Definition drift: KPIs and column meaning change without a shared source of truth.
- Schema risk is invisible: a rename upstream breaks five dashboards downstream.
- Compliance anxiety: PII tags and usage paths aren’t obvious at audit time.
- Evidence is manual: people screenshot, paste, and re-enter the same facts.
The chasm between roles
Data Stewards / Governance
Need visibility into what data is sensitive, where it flows, and whether policies are enforced.
Data Product Owners
Need clarity on reliability & adoption—SLAs, incidents, and the blast radius of change.
Data Engineers
Need fast answers to what breaks if I change this, with upstream/downstream maps and owners.
How we bridge the gap
One workflow that turns facts into guardrails and shared maps.
- M Metadata: one catalog of datasets, owners, domains, sensitivity; exportable DDLs per target DB.
- P Profile: automatic types, ranges & quartiles, null%, top-k categories, valid value sets.
- Q Quality: generate range/regex/enum/NOT NULL checks, track incidents & SLAs with evidence.
- L Lineage: upstream/downstream and cross-domain impact—ownership, risk, and blast radius at a glance.
Journey
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Internal workbench → real teams
Started as a pragmatic toolkit to tame schema sprawl and speed safe changes.
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M • P • Q • L comes together
Bundled metadata, profiling, quality, and lineage into one opinionated flow.
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Pilots & proof
Turned profiling findings into enforceable checks; reduced incident MTTR with visible lineage.
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Public release
Export-first product with clean pages for Metadata, Profile, Quality, and Lineage.
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What’s next
More connectors, policy evidence packs, and deeper impact analytics across domains.
Who we serve
- Data engineering & platform teams
- Analytics & BI leads
- Governance & data stewards
- Technology leaders who need clear impact & ROI
Outcomes
- Faster incident triage with clear ownership and impact
- Higher trust in metrics & definitions
- Lower risk through visible controls and evidence
- Shorter cycle time from change to value
See M → P → Q → L in action
Bring a dataset to the demo—we’ll profile it, generate rules, and map its lineage live.