Overview

Greetings from ZettaMine!!!

Responsibilities

• Own the Feature Store population pipeline, ingesting Behavior Signals, Insurance Coverage Signals, Clinical Signals, Engagement Signals, Rx Signals, and Contextual Features from curated BigQuery data marts into the GCP Feature Store.
• Design and maintain ML training dataset pipelines for NBA and AI Insight models, including offline batch paths, online serving feature paths, training/evaluation splits, and dataset versioning.
• Integrate Adobe Analytics event data as a behavioral signal source, aligning it with clinical and benefits data for multi-source model training.
• Operate and tune Dataproc Spark Jobs / Dataproc for large-scale feature engineering and model training data preparation.
• Monitor feature freshness, training data drift, and model data quality in partnership with Vertex AI pipelines.
• Collaborate with backend domain engineers to define Kafka event schemas that feed ML operations.
• Implement HIPAA-compliant handling of PHI across ML training datasets. Apply data classification tagging, field-level masking, and de-identification before model training. Enforce role-based access controls on Feature Store entries and maintain audit trails for PHI access and lineage.
• Support Disaster Recovery (DR) zone design for ML data assets. Coordinate cross-region replication of training datasets and Feature Store snapshots, define RTO/RPO for ML pipeline recovery, and participate in DR drills.
• Implement data archival policies for ML training datasets. Manage BigQuery dataset expiration schedules, archive historical training snapshots to tiered Cloud Storage, and ensure HIPAA-mandated retention windows are met for PHI-derived features.

Qualifications

• 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 / Dataproc / PySpark for large-scale feature engineering.
• Familiarity with Adobe Analytics data structures and integration patterns.
• Solid BigQuery skills, including complex SQL, window functions, and ML-optimized table design.
• Understanding of the ML lifecycle, feature engineering, data versioning, and train/evaluation splits.
• Hands-on experience handling PHI under HIPAA, including 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.
• Interested candidates kindly share your updated CV to aruna.m@zettamine.com or WhatsApp to 6302404863.
• Thanks & Regards,