Data Enginner with ML

Position : Data Engineer with ML 5-7 years of hands-on data or ML data engineering experience in a production GCP environment. Strong proficiency in Python, Java, or Node.js for pipeline development, feature engineering scripts, and ML data tooling. Strong experience building ML training pipelines and Feature Stores (GCP Feature Store preferred). Deep proficiency with Apache Spark (DataSpark/PySpark) for large-scale feature engineering. Experience with Kafka for consuming streaming event signals into ML pipelines. Familiarity with Adobe Analytics data structures and integration patterns. Solid BigQuery skills: complex SQL, window functions, ML-optimized table design. Understanding of ML lifecycle: feature engineering, data versioning, train/eval splits. Experience with Vertex AI Pipelines or similar MLOps tooling. Hands-on experience handling PHI under HIPAA: de-identification techniques (Safe Harbor, Expert Determination), field-level masking in training data, and audit logging for PHI access in ML systems. Familiarity with disaster recovery planning for data platforms: cross-region replication, RTO/RPO definition, and recovery testing. Experience with data archival and retention for ML datasets: tiered storage policies and HIPAA retention compliance.