Lead Data Engineer

Lead Data Engineer
Broad areas of responsibility:
• Own and evolve the enterprise data architecture supporting operational, analytical, and AI workloads.
• Design scalable data platforms for transactional databases, analytical data warehouses/lakehouses, and AI/ML data pipelines.
• Define data models, integration standards, metadata management, and data lifecycle strategies.
• Establish best practices for data engineering, architecture, performance optimization, scalability, reliability, and maintainability.
• Evaluate and recommend emerging technologies and architectural improvements.
• Design and maintain data platforms supporting Business Intelligence, advanced analytics, and machine learning workloads.
• Build data pipelines that enable AI/ML model training, feature engineering, vector databases, Retrieval-Augmented Generation (RAG), and LLM/SLM applications.
• Design, develop, and optimize robust ETL/ELT pipelines for structured and unstructured data.
• Build reliable batch and real-time data integration pipelines from EHRs, Practice Management Systems, APIs, flat files, and third-party healthcare applications.
• Lead, mentor, and develop a team of Data Engineers.
• Conduct code reviews, architecture reviews, and technical design discussions.
Personal Profile:
• Bachelor’s degree in computer science, Information Technology, Engineering, or related discipline.
• 6+ years of experience in Data Engineering, Data Platform Engineering, or Data Architecture.
• Experience with Apache NiFi, Airflow, dbt, Spark, Kafka, or equivalent modern data engineering tools.
• Experience with Power BI or other enterprise BI platforms.
• Familiarity with healthcare interoperability standards such as HL7 and FHIR.
• Experience implementing data platforms for AI/ML and Generative AI workloads.
• Knowledge of CI/CD, Infrastructure as Code, and DevOps practices. • Experience with containerized workloads (Docker, Kubernetes).

Work arrangement
Yes