Lead Data Engineer

HealthRecon Connect provides technology-enabled Revenue Cycle Management solutions to US healthcare providers. The company leverages over 30 years of deep domain expertise, machine learning, AI, cutting-edge analytics, and automated workflows that help improve cash flow, patient outcomes and enable peace of mind for their clients. At HealthRecon Connect, day after day, we not only hold ourselves accountable for setting and maintaining high standards, but we also passionately strive for the highest achievement, customer delight and thrive on the challenge of high expectations and commitment to excel.

HealthRecon was certified a Great Workplace by Great Place to Work® Sri Lanka since 2018 and was adjudged one of the 40 Best Workplaces in Sri Lanka by Great Place to Work® Sri Lanka in 2021. We are also a participant of the United Nations Global Compact.

HRC Labs was established to lead the technological transformation of HealthRecon Connect (HRC). Propelled by the deep domain expertise and industry leading service capability of HRC, HRC Labs focus on enhancing the efficiency of healthcare delivery through intelligent automation solutions for healthcare providers. Our tools sustainably improve clients’ operating margins and cash flows by compressing their working capital cycle and reducing their administrative burden.

We are currently looking for a Lead Data Engineer to join HRC focused on Revenue Cycle Management (RCM) technology automations and solutions.

Due to the large volume of applications we receive, all applications will be reviewed in the order in which they were received and only the candidates short-listed for the first round of interviews will be contacted. Thank you for your understanding.

Job Vacancy:Lead Data Engineer

Work Week:Monday to Friday

Shift Window:03:00 PM – 12:00 AM SLST (Straddle Shift)

Important: HealthRecon Connect currently operates under a hybrid work arrangement, with the number of remote workdays varying by team. However, depending on client deliverables and business needs, employees may be required to work on-site for all five weekdays.

By applying, you acknowledge and agree to be available for in-person work five days a week if required.

Other Features:

Full-timeUS calendar applicable

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.
• 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.
• 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.