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

Qualifications/Criteria

- Bachelor’s degree in computer science, Software Engineering, or a related field.

- 06+ years of experience in Data Engineering, Data Platform Engineering, or Data Architecture.

- Minimum 3 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.

- Robust 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.

📌 Senior Data Engineer (India)
🏢 HealthRecon Connect
📍 India