Data Engineer (AI/ML)

Job Overview The Data Engineer will design, build, and optimize scalable, secure data pipelines that power analytics and product platforms. The focus will be on Machine Learning (ML) and Generative Artificial Intelligence (GenAI) workloads, 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. Participate in code reviews, documentation, and continuous improvement efforts across the engineering team. 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. Qualifications 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. Technical Skills & Knowledge Experience with building and operationalizing data foundations that support ML 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, 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). Proficiency in Python, SQL, and distributed data frameworks (PySpark, Databricks, AWS Glue, EMR). Working knowledge of cloud platforms (AWS or Azure) and data warehouses (Snowflake). Familiarity with NoSQL and relational databases, as well as data modeling best practices. Strong analytical, problem‑solving, and communication skills. 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. Compliance & Healthcare Knowledge: Knowledge of SOC 2, HIPAA, GDPR compliance; understanding of healthcare data security principles and standards such as HL7 and FHIR. Compensation Salary range: 100,800.00 - 138,600.00 Benefits Paid time off, 11 holidays, medical/dental/vision insurance, generous 401(k) matching, lifestyle spending account and many other benefits to eligible employees. #J-18808-Ljbffr