Senior Data Engineer - Data Impact & Governance

About the Role

Key Responsibilities

• Build and scale AI/ML data pipelines by designing, implementing, and maintaining batch and streaming data pipelines supporting ML training, deployment, inference, and monitoring using Azure, Dataiku, and open-source tools.
• Deploy and manage raw data, feature, and vector stores to enable fast, reliable access to feature data for production AI/ML systems.
• Use Infrastructure as Code (IaC) and CI/CD workflows to automate infrastructure and pipeline deployments, improving reliability and efficiency across environments.
• Implement validation, lineage, anomaly detection, and drift monitoring to ensure data quality, freshness, accuracy, and compliance.
• Enforce security and compliance measures such as encryption, RBAC, tokenization, and audit logging to meet HIPAA/HITRUST standards while enabling scalable AI operations.
• Partner with healthcare data engineers, ML engineers, data scientists, product teams, and application owners to deliver scalable AI solutions, providing mentorship and fostering best practices.
• Own and operate pipelines and infrastructure end-to-end, including monitoring, alerting, incident management, and continuous improvement.

Requirements

• Bachelor's degree in a relevant field.
• Five years of relevant information technology experience (or three years with a master's degree).
• Proficiency in Python and SQL for large-scale data engineering, and Spark for distributed processing.
• Experience with Azure cloud services (Fabric OneLake, Synapse, blob storage) and on-prem RDBMS, NEO4J, Mongo.
• Knowledge of pipeline orchestration tools such as Airflow and Dataiku, and high-volume processing with Spark/Fabric.
• Experience with feature and vector stores like Feast, Pinecone, PGVector, Azure Feature Store.
• Familiarity with data integration and streaming technologies such as CDC, Kafka, Event Hubs, HL7, FHIR, DICOM.
• Deployment automation skills using Terraform, Bicep, Helm, GitHub Actions, and Azure DevOps.
• Monitoring and observability expertise including data validation, lineage, anomaly detection, and pipeline monitoring.
• Understanding of security and compliance practices including encryption, RBAC, audit logging, HIPAA/HITRUST.
• Experience with exploratory and descriptive analytics, data profiling, statistical inference, and trend analysis.
• Deep understanding of healthcare datasets, standards (HL7, FHIR, DICOM), and experience with extraction, normalization, and de-identification for analytics.
• Strong communication skills for collaborating with research scientists, ML engineers, and stakeholders, and documenting processes.

Nice to Have

• Master's level degree.
• Epic Data Model certification (Clinical, Access, or Revenue) within 180 days.
• Azure Data Engineer Associate (DP-203).
• EPIC Cogito Certification.
• HIPAA Privacy & Security Certification.
• HL7/FHIR Certification.
• Experience in a Senior Data Scientist role in AI/ML space.
• Knowledge of data privacy, security, and HIPAA compliance in healthcare.

Qualifications

• Bachelor's degree required.
• Preferred Master's degree.
• Minimum of five years of relevant IT experience; three years if holding a master's degree.

Benefits & Perks

• Salary range from USD 123,000 to USD 185,000 depending on experience.
• Full-time, regular employment with workweek during days.
• Remote work within Texas.
• Referral bonus and relocation assistance available.
• Pivotal position with opportunities for impact in healthcare AI.