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 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:
Data Engineer
Work Week:
Monday to Friday
Shift Window:
3:00 PM – 12:00 PM 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-time
US calendar applicable
Responsibilities:
- Develop, maintain, and enhance ETL/ELT pipelines for ingesting, transforming, and loading data from multiple sources into centralized data platforms.
- Design and implement performance-optimized and cost-efficient ELT workflows, minimizing compute usage, optimizing data scans, and reducing processing time.
- Work with structured and unstructured data to build scalable data models and datasets used by analysts, data scientists, and business teams.
- Collaborate with cross-functional teams to understand data requirements and implement solutions that align with business needs.
- Develop and optimize complex SQL queries, stored procedures, and transformation logic for analytics and operational use cases.
- Apply advanced query optimization techniques (partitioning, indexing, clustering, pruning, incremental loads) to improve performance.
- Assist in building and maintaining data lakes, data warehouses, and cloud-based data platforms (AWS/GCP/Azure).
- Optimize storage and compute usage by implementing data partitioning, compression, and lifecycle management strategies.
- Implement data quality validations, monitoring processes, and automated checks to ensure reliability and consistency of data assets.
- Work with tools and frameworks such as Apache Spark, AWS Glue, Airflow, or similar orchestration platforms to automate workflows.
- Support both real-time (streaming) and batch processing systems, ensuring efficient resource utilization and scalability.
- Troubleshoot data pipeline issues and perform root cause analysis for performance bottlenecks and failures.
- Ensure proper documentation of data flows, transformations, and system configurations.
- Follow best practices in data governance, security, cost optimization, version control, and CI/CD processes.
- Participate in Agile development practices, including sprint planning, stand-ups, and retrospectives.
Qualifications/Criteria:
- Bachelor’s degree in computer science, Information Technology, Engineering, or related discipline.
- 2–4 years of experience in data engineering or a related field.
- Hands-on experience developing and maintaining ETL/ELT workflows in production environments.
- Demonstrated experience in optimizing data pipelines for performance and cost efficiency.
- Strong experience working with SQL, data pipelines, and cloud-based data systems.
- Exposure to big data frameworks, data warehousing concepts, and data modeling projects.
- Certifications in cloud platforms (AWS/GCP/Azure) or data engineering tools are beneficial but not required.
- Experience working with APIs, JSON, CSV, Parquet, and other data formats.
- Knowledge of CI/CD, Git, and collaborative development workflows.