Data Engineer (AI/ML)

About the role

The Data Engineer will design, build, and optimize scalable, secure data pipelines that power analytics and product platforms. Specifically, the focus will be on Machine Learning (ML) and Generative Artificial Intelligence (GenAI) workloads, while contributing to innovation and ensuring compliance with healthcare industry standards.

Responsibilities

• Design, build, and maintain reliable, high-performance data pipelines for large-scale structured and unstructured healthcare data.
• Use PySpark and modern cloud-based tools (Databricks, AWS Glue, EMR, Snowflake) to transform and process data efficiently.
• Support ingestion, transformation, and validation processes that ensure data consistency, integrity, and availability.
• Partner with Data Architects, Data Scientists, and Analysts to translate business needs into scalable engineering solutions.
• Collaborate with platform and DevOps teams to deploy, scale, and monitor data pipelines using Airflow and Kubernetes.
• Implement and maintain data validation frameworks to ensure pipeline accuracy and completeness.
• Contribute to best practices in version control, metadata management, and reproducibility.
• Stay current with emerging technologies in data engineering and cloud computing, recommending improvements to existing infrastructure.
• Participate in performance tuning, cost optimization, and scaling strategies for cloud-based data systems.
• Identify automation opportunities to streamline ETL/ELT processes and reduce operational overhead.
• Share knowledge and mentor junior team members on tools, techniques, and best practices.
• Promote a culture of collaboration, innovation, and continuous learning within the engineering organization.
• Support compliance with SOC 2, HIPAA, and GDPR by adhering to established data privacy and security practices.

Requirements

• Education: Bachelor’s or Master’s degree in Computer Science, Engineering, or related field.
• Experience: 5+ years of experience in data engineering, including building and managing pipelines in cloud-based environments.
• Skills: Experience with building and operationalizing the data foundations that support machine learning and generative AI use cases, including feature pipelines, training/inference data preparation, and retrieval-ready datasets (e.g., embeddings and vector stores).
• Familiarity: Familiarity with GenAI skills and adjacent tooling (foundation models, prompt engineering, RAG, embeddings/vector databases, and GenAI orchestration frameworks).
• Tools: Hands-on experience with AWS AI/ML and data services, including Amazon Bedrock, Bedrock Agent Core, SageMaker, Glue, and EMR.
• Data Architecture: Experience designing and optimizing data architectures, including data foundations that support ML and GenAI workloads.
• Workflow Orchestration: Hands-on experience with workflow orchestration (Airflow) and containerization (Kubernetes).
• Technical Expertise: Strong hands-on technical expertise, cross-team collaboration, and contributing to architecture decisions.
• Languages: Proficiency in Python, SQL, and distributed data frameworks (PySpark, Databricks, AWS Glue, EMR).
• Cloud Platforms: Experience with cloud platforms (AWS or Azure) and data warehouses (Snowflake).
• Databases: Hands-on experience with NoSQL and relational databases, as well as data modeling best practices.
• Compliance: Understanding of compliance frameworks (SOC 2, HIPAA) and secure data management principles.
• Healthcare: Experience working with healthcare datasets or knowledge of healthcare standards (HIPAA, HL7, FHIR) preferred.

Qualifications

Experience with building and operationalizing the data foundations that support machine learning and generative AI use cases, including feature pipelines, training/inference data preparation, and retrieval-ready datasets (e.g., embeddings and vector stores).

Familiarity with GenAI skills and adjacent tooling (foundation models, prompt engineering, RAG, embeddings/vector databases, and GenAI orchestration frameworks).

Hands-on experience with AWS AI/ML and data services, including Amazon Bedrock, Bedrock Agent Core, SageMaker, Glue, and EMR.

Experience designing and optimizing data architectures, including data foundations that support ML and GenAI workloads.

Hands-on experience with workflow orchestration (Airflow) and containerization (Kubernetes).

Strong hands-on technical expertise, cross-team collaboration, and contributing to architecture decisions.

Proficiency in Python, SQL, and distributed data frameworks (PySpark, Databricks, AWS Glue, EMR).

Experience with cloud platforms (AWS or Azure) and data warehouses (Snowflake).

Hands-on experience with NoSQL and relational databases, as well as data modeling best practices.

Understanding of compliance frameworks (SOC 2, HIPAA) and secure data management principles.

Experience working with healthcare datasets or knowledge of healthcare standards (HIPAA, HL7, FHIR) preferred.

Skills

• Python
• SQL
• Distributed Data Frameworks (PySpark, Databricks, AWS Glue, EMR)
• AWS AI/ML and data services (Amazon Bedrock, Bedrock Agent Core, SageMaker, Glue, EMR)
• Airflow
• Kubernetes
• Feature Pipelines
• Training/Inference Data Preparation
• Retrieval-Ready Datasets (embeddings and vector stores)
• Foundation Models
• Prompt Engineering
• RAG
• Embeddings/Vector Databases
• GenAI Orchestration Frameworks
• Cloud Platforms (AWS or Azure)
• Data Warehouses (Snowflake)
• NoSQL Databases
• Relational Databases
• Data Modeling Best Practices
• SOC 2 Compliance
• HIPAA Compliance
• Healthcare Standards (HIPAA, HL7, FHIR)

Benefits

Comprehensive benefits package including paid time off, 11 holidays, medical/dental/vision insurance, generous 401(k) matching, lifestyle spending account, and many other benefits.

Pay

The posting range for this position is $100,800.00 - $138,600.00.

Schedule

Not specified.

Application Instructions

Not specified.