Daniel Vianna is a versatile engineer who turns tangled business workflows and legacy systems into testable, independently deployable services, with 11 years of experience spanning software, data, and platform engineering. He specialises in finding stable boundaries between product, pipelines, and operational concerns—reducing costs, improving observability, and accelerating feedback loops across cloud-native SaaS platforms. Notable wins include separating local-testable Spark models, introducing low-code ingestion via Airbyte on Kubernetes, and moving multi-tenant voice analytics from per-customer deployments to a row-level secure PostgreSQL architecture that cut cloud costs dramatically. Comfortable in Python, Scala/Spark, Haskell and frontend stacks, he bridges data-science implementations and production code by shipping APIs that preserve model behaviour. Based in Melbourne, his background in experimental neuroscience informs a methodical approach to noisy signals and stakeholder translation, turning messy constraints into repeatable, auditable systems.
Contributions:36 commits, 54 pushes, 5 branches in 2 months
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