Job Description

Responsibilities

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- Own and evolve the enterprise data architecture supporting operational, analytical, and AI workloadsn
- Design scalable data platforms for transactional databases, analytical data warehouses/lakehouses, and AI/ML data pipelinesn
- Define data models, integration standards, metadata management, and data lifecycle strategiesn
- Establish best practices for data engineering, architecture, performance optimization, scalability, reliability, and maintainabilityn
- Evaluate and recommend emerging technologies and architectural improvementsn
- Design, develop, and optimize robust ETL/ELT pipelines for structured and unstructured datan
- Build reliable batch and real-time data integration pipelines from EHRs, Practice Management Systems, APIs, flat files, and third-party healthcare applicationsn
- Develop and optimize workflows using tools such as Apache NiFi or equivalent orchestration platformsn
- Ensure high data quality, integrity, consistency, lineage, and observability across all data platformsn
- Support relational, NoSQL, and distributed data platformsn
- Design and maintain data platforms supporting Business Intelligence, advanced analytics, and machine learning workloadsn
- Build data pipelines that enable AI/ML model training, feature engineering, vector databases, Retrieval-Augmented Generation (RAG), and LLM/SLM applicationsn
- Collaborate with Data Scientists and AI Engineers to operationalize ML models and AI solutionsn




- Support MLOps and data versioning best practicesn

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Qualifications/Criteria

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

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