Create an engineering framework and automation that converts data science proofs of concept (notebooks, Python code, features, models, and validation logic) into modular, testable, version-controlled, deployable software, including CI/CD, validation, promotion, observability, rollback, and lineage. Strong understanding of distributed systems, performance engineering, data formats such as Iceberg, Delta Lake, Avro, and Parquet, and Lakehouse, batch, and streaming architectures, plus hands-on knowledge of Git, Maven or Gradle, automated testing, Agile, and DevOps practices.