Sr. Data Engineer

Clear ownership, not decision by consensus

First principles over inherited patterns

Shipping systems, not slide decks

Fast feedback from reality, not opinions

Own and evolve the data lifecycle across systems, from ingestion through production-ready ontology

Ingest data from databases, data streams, batch files, and incremental feeds

Define standards for and operate medallion-style lakehouse pipelines (bronze → silver → gold)

Transform raw inputs into a consistent digital twin of supply chain reality that scales across customers

Serve high-quality data to analytics, AI workflows, and write-back systems with clear correctness guarantees

Own data correctness and reliability in production, including monitoring, on-call, incident response, and post-incident systemic improvements

Define and enforce data quality checks, validations, and robust backfill strategies

Use AI-assisted tools responsibly, setting expectations for review, validation, and production readiness of generated code

Reduce complexity at scale by simplifying pipelines, eliminating redundancy, and automating recurring workflows

Partner with product, science, and platform tooling teams to translate ambiguous needs into durable technical designs

Degree in Computer Science, Mathematics, Statistics, or other data-intensive discipline with substantive engineering experience

5+ years demonstrated development experience using technologies like Python, SQL, Scala, Spark, Flink, and Beam