Job Description:
SUMMARY:
- Develop and implement a strategic data analytics roadmap for the healthcare payer business, aligned
- with overall business objectives.
- Design and execute complex data analysis projects focused on areas like risk rating, claims
- adjudication, and enrollment optimization.
- Conduct statistical analysis and modeling to identify trends, patterns, and key insights from
- healthcare payer data.
- Minimum 5 years of experience in healthcare payer analytics, with a proven track record of success
- in leading and delivering impactful projects.
- Strong understanding of risk adjustment methodologies (e.g., Hierarchical Condition Category (HCC)
- coding) and their impact on healthcare payer reimbursement.
- In-depth knowledge of healthcare claims and enrollment data structures and processes.
- Proven experience utilizing big data technologies like Hadoop, Spark, or similar on cloud platforms
- like AWS.
- Proficiency in programming languages like Scala, Python, or R for data manipulation and analysis.
- Excellent communication, presentation, and interpersonal skills with the ability to effectively
- translate technical findings to a non-technical audience.
KEY DUTIES AND RESPONSIBILITIES:
- Design, develop, and maintain robust data pipelines using Python and PySpark to process large
- volumes of healthcare data efficiently in a multitenant analytics platform.
- Collaborate with cross-functional teams to understand data requirements, implement data models,
- and ensure data integrity throughout the pipeline.
- Optimize data workflows for performance and scalability, considering factors such as data volume,
- velocity, and variety.
- Implement best practices for data ingestion, transformation, and storage in AWS services such as S3,
- Glue, EMR, and Redshift.
- Model data in relational databases (e.g., PostgreSQL, MySQL) and file-based databases to support
- data processing requirements.
- Design and implement ETL processes using Python and PySpark to extract, transform, and load data
- from various sources into target databases.
- Troubleshoot and enhance existing ETLs and processing scripts to improve efficiency and reliability of
- data pipelines.
- Develop monitoring and alerting mechanisms to proactively identify and address data quality issues
- and performance bottlenecks.
EDUCATION AND EXPERIENCE:
- Bachelor's or Master's degree in Computer Science, Data Engineering, or a related field with Minimum 9 years of experience.
- Minimum of 5 years of experience in data engineering, with a focus on building and optimizing data
- pipelines.
- Expertise in Python programming and hands-on experience with PySpark for data processing and
- analysis.
- Proficiency in Python frameworks and libraries for scientific computing (e.g. Numpy, Pandas, SciPy,
- Pytorch, Pyarrow).
- Strong understanding of AWS services and experience in deploying data solutions on cloud platforms.
- Experience working with healthcare data, including but not limited to eligibility, claims, payments,
- and risk adjustment datasets.
- Expertise in modeling data in relational databases (e.g., PostgreSQL, MySQL) and file-based
- databases, ETL processes and data warehousing concepts.
- Proven track record of designing, implementing, and troubleshooting ETL processes and processing
- scripts using Python and PySpark.
- Excellent problem-solving skills and the ability to work independently as well as part of a team.
- Relevant certifications in AWS or data engineering would be a plus.
- Expertise in Python programming language for data processing and analysis.
- Expertise in PySpark for building scalable data pipelines.
- In-depth knowledge of AWS services such as S3, Glue, EMR, and Redshift for data storage and
- processing.
- Familiarity with relational databases (e.g., PostgreSQL, MySQL) and file-based databases for data
- modeling and storage.
- Understanding of data modeling, ETL processes, and data warehousing concepts.
- Knowledge of best practices in data engineering and experience in optimizing data workflows for
- performance and scalability.
- Experience in healthcare data domains, including eligibility, claims, payments, and risk adjustment
- datasets.
- Up-to-date knowledge of emerging technologies and trends in data engineering.
- Strong problem-solving skills and the ability to troubleshoot and optimize data pipelines and ETL
- processes.
- Excellent communication and collaboration skills to work effectively with cross-functional teams.
- Proficient in designing, implementing, and maintaining data pipelines for processing large volumes of
- data.
- Ability to model data in relational and file-based databases to support data processing requirements.
- Skill in developing monitoring and alerting mechanisms to ensure data quality and pipeline reliability.
- Experience in deploying data solutions on cloud platforms and utilizing AWS services for data
- processing.
- Proficiency in writing efficient and maintainable code for data processing tasks.
- Ability to stay organized, prioritize tasks, and meet project deadlines effectively.
- Ability to work independently and in a team-oriented, collaborative environment.
- Strong analytical skills to identify and address data quality issues and performance bottlenecks.
- Capability to innovate and recommend solutions for continuous improvement in data engineering
- processes.
- Ability to communicate complex technical concepts to non-technical stakeholders effectively.
- Strong attention to detail and commitment to delivering high-quality work.
- Ability to deal with problems involving several concrete variables in standardized situations.
- Ability to interact politely, tactfully and firmly with a wide range of people and personalities.
- Ability to work in an environment with potential interruptions.
- Ability to manage multiple simultaneous tasks with individual timeframes and priorities.
Healthcare Experience:
Must have:
- 5+ years of experience in healthcare data Analytics, preferably in a health insurance payer, hospital,
- health system, managed care organization, or consulting firm
- Strong understanding of healthcare terminology, regulations, and compliance requirements (e.g.,
- HIPAA, CMS guidelines)
- Experience with healthcare quality metrics, performance measurement, and reporting methodologies
- Knowledge of healthcare reimbursement systems, revenue cycle management, and financial analysis
- principles
- Familiarity with healthcare information technology (IT) systems, electronic health records (EHRs), and
- health information exchanges (HIEs)
- Ability to communicate complex healthcare data and findings effectively to diverse stakeholders,
- including executives, clinicians, and non-technical staff
Good to have:
- Experience working with interdisciplinary teams and collaborating with healthcare providers,
- administrators, and IT professionals
- Passion for improving healthcare quality, efficiency, and patient outcomes through data-driven insights
- and evidence-based practices
- Commitment to continuous learning and professional development in the evolving field of healthcare
- analytics
- Certification in healthcare data analytics (e.g., Certified Health Data Analyst - CHDA) or related
- credentials is a plus