IntraEdge
Senior Data Engineer
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Senior Data Engineer with 5–7 years of experience in GCP, strong Python/Java/Node.js skills, and proficiency in Apache Spark. Contract length is unspecified, with a competitive pay rate. Experience with HIPAA compliance and ML pipelines is required.
🌎 - Country
United States
💱 - Currency
$ USD
-
💰 - Day rate
Unknown
-
🗓️ - Date
July 1, 2026
🕒 - Duration
Unknown
-
🏝️ - Location
Unknown
-
📄 - Contract
Unknown
-
🔒 - Security
Unknown
-
📍 - Location detailed
United States
-
🧠 - Skills detailed
#Datasets #PySpark #Replication #Java #GCP (Google Cloud Platform) #Python #Spark (Apache Spark) #Disaster Recovery #ML (Machine Learning) #Apache Spark #SQL (Structured Query Language) #Compliance #BigQuery #Kafka (Apache Kafka) #Storage #AI (Artificial Intelligence) #Data Engineering
Role description
• 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
• 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
• 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
• 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
