Responsibilities

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, 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
Develop and optimize workflows using tools such as Apache NiFi or equivalent orchestration platforms
Ensure high data quality, integrity, consistency, lineage, and observability across all data platforms
Support relational, NoSQL, and distributed data platforms
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
Collaborate with Data Scientists and AI Engineers to operationalize ML models and AI solutions




Support MLOps and data versioning best practices

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Qualifications/Criteria

Bachelor’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.
Strong 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.
Solid 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 (Erode)
🏢 HealthRecon Connect
📍 Erode