ResponsibilitiesOwn and evolve the enterprise data architecture supporting operational, analytical, and AI workloadsDesign scalable data platforms for transactional databases, analytical data warehouses/lakehouses, and AI/ML data pipelinesDefine data models, integration standards, metadata management, and data lifecycle strategiesEstablish best practices for data engineering, architecture, performance optimization, scalability, reliability, and maintainabilityEvaluate and recommend emerging technologies and architectural improvementsDesign, develop, and optimize robust ETL/ELT pipelines for structured and unstructured dataBuild reliable batch and real-time data integration pipelines from EHRs, Practice Management Systems, APIs, flat files, and third-party healthcare applicationsDevelop and optimize workflows using tools such as Apache NiFi or equivalent orchestration platformsEnsure high data quality, integrity, consistency, lineage, and observability across all data platformsSupport relational, NoSQL, and distributed data platformsDesign and maintain data platforms supporting Business Intelligence, advanced analytics, and machine learning workloadsBuild data pipelines that enable AI/ML model training, feature engineering, vector databases, Retrieval-Augmented Generation (RAG),



and LLM/SLM applicationsCollaborate with Data Scientists and AI Engineers to operationalize ML models and AI solutionsSupport MLOps and data versioning best practices.Qualifications/CriteriaBachelor’s degree in computer science, Software Engineering, or a related field.10+ years of experience in Data Engineering, Data Platform Engineering, or Data Architecture.Minimum 5 years of experience working with US Healthcare data, preferably Revenue Cycle Management (RCM), Claims, EHR, or Healthcare Analytics.Equivalent practical experience with demonstrated technical leadership will also be considered.Proven experience designing enterprise-scale data architecture.Robust expertise in SQL and data modeling.Hands-on experience with relational databases (PostgreSQL, SQL Server, MySQL, Oracle) and analytical databases/warehouses.Experience building scalable ETL/ELT pipelines and workflow orchestration.Strong knowledge of batch and streaming data processing.Experience with Python for data engineering and automation.Experience designing cloud-based data platforms (AWS, Azure, or GCP).Working knowledge of modern data lake house architectures.Understanding of AI/ML data engineering concepts, including feature stores, vector databases, embeddings, LLMs, and SLMs.Strong understanding of data governance, metadata management, data quality, security, and access control.Excellent problem-solving, communication, and stakeholder management skills.

📌 Lead Data Engineer (Narela)
🏢 HealthRecon Connect
📍 Narela