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kg / Ketut Garjita
01 / Engineering experience

Systems owned.
Evidence shown.

A hiring-team view of Ketut Garjita’s engineering context: the systems operated, decisions made, collaborators involved, and outcomes that can be verified.

A timeline built for context.

Titles alone do not explain engineering impact. Each entry is designed to connect a role to its operating surface: what was owned, which decisions mattered, and where the evidence lives.

Timeline / awaiting verified record

Experience details to be added

Employer or organisation: not published Dates: not published Location: not published

No employer, role title, date range, client, metric, or accomplishment was supplied for this portfolio. Rather than manufacture a résumé, this timeline keeps the missing evidence visible and gives each future role a clear structure.

See the evidence structure for each role

Scope

Systems, data domains, environments, scale, and the boundary between individual and team ownership.

Decisions

Architecture choices, trade-offs, reliability practices, and collaboration with product or engineering partners.

Outcomes

Measured results where available, with a source or qualification instead of an unsupported performance claim.

role title · pending systems owned · pending outcome source · pending

Responsibility map

These are the engineering surfaces this portfolio is prepared to document. They are connected to the Skills taxonomy, but are not presented as past employment claims until the owner’s records support them.

Use the linked taxonomy to inspect the vocabulary and tools that should accompany a verified role entry. The distinction matters: capability is not the same as a claim about production ownership.

R01

Pipeline design

Ingestion boundaries, batch or streaming choices, transformation stages, failure handling, and the contracts that let downstream users trust a dataset.

See skills taxonomy →
R02

Orchestration

Dependencies, retries, observability, backfills, and operational ownership across scheduled data workflows.

See skills taxonomy →
R03

Data modelling

Analytical grain, dimensional decisions, schema evolution, naming conventions, and models designed for their actual consumers.

See skills taxonomy →
R04

Data quality

Validity, completeness, freshness, reconciliation, and the feedback loop between a failed check and a responsible owner.

See skills taxonomy →
R05

Cloud operations

Environment boundaries, permissions, cost awareness, deployment paths, and the practical controls that keep data systems maintainable.

See skills taxonomy →
R06

AI data preparation

Dataset curation, provenance, evaluation inputs, retrieval context, and the quality controls needed before data reaches an AI workflow.

See skills taxonomy →
Evidence paths

Follow the work, not the headline.

When a role record is published, this area will connect its claims to inspectable project evidence.

Operating principles

Trust is an engineering feature.

01

Separate capability from evidence

A tool appearing in a skills list is not treated as proof of production ownership. Role claims belong beside their scope and source.

02

Name the boundary

Good engineering context includes what a person owned, what the team owned, and which decisions required collaboration.

03

Prefer measurable outcomes

Reliability, latency, cost, freshness, adoption, and delivery outcomes are more useful than inflated adjectives—when the measurement is available.

04

Make the next conversation easy

A recruiter or engineering lead should be able to move from a claim to a project, from a project to a skill, and from there to a direct conversation.

Next step / direct context

Have a role, system, or collaboration in mind?

For the current contact route, reach Ketut through the portfolio contact page. LinkedIn is the stated professional channel; a profile link can be added here once the owner’s canonical URL is confirmed.

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