Ingestion
Define what arrives, from where, with which schema, freshness expectation, and failure behavior. Capture is part of the contract—not an invisible prelude.
AI data engineering / portfolio
Ketut Garjita
Building reliable data foundations for AI: pipelines that are observable, transformations that are explainable, and delivery paths that engineering teams can trust.
pipeline / inspectable path
ready for review
Sourcesevents · files · APIs
Ingestcontracts · capture
Orchestratedependencies · retries
Transformbatch · distributed
Qualitytests · lineage
AI deliveryfeatures · datasets
01 / evidence first
Work should be inspectable.
Project claims belong beside their goals, architecture, tools, outcomes, source code, and live demonstrations. No verified project records or external URLs were supplied for this portfolio yet, so this page does not invent names, metrics, employers, repositories, or demos.
02 / engineering approach
The layer beneath the model.
AI systems inherit the strengths and weaknesses of the data path beneath them. A dependable workflow makes each handoff visible, testable, and understandable before a dataset reaches a model or decision.
Orchestration
Represent dependencies explicitly so retries, backfills, scheduling, and ownership are operational decisions rather than accidental behavior.
Transformation
Use Python for expressive pipeline logic and Apache Spark when distributed computation is justified by the data shape and workload.
Quality
Check schema, null behavior, uniqueness, ranges, freshness, and reconciliation at the boundaries where bad data can be stopped cheaply.
Observability
Make runs explainable through logs, task state, lineage, alerts, and useful failure context—the information needed to repair a pipeline at 02:00.
Delivery
Separate production-ready datasets, feature inputs, and analytical outputs from transient processing so downstream consumers know what they can trust.
03 / technology surface
Filter the system view.
These are technology areas to explore across the portfolio. The filter is intentionally small: it helps a reviewer move from a broad stack to the engineering concern behind it.
all areas
From raw movement to reliable use.
Python supports pipeline logic and automation; Apache Spark supports distributed transformation; Apache Airflow makes dependencies and operations explicit; cloud platforms provide scalable storage and compute surfaces. Review the Skills page for the full technology map.
04 / open channel
Have a data path worth improving?
Use LinkedIn as the professional contact route, or leave the context below so a future conversation starts with a useful technical brief.
Open contact page