Sr. Data Engineer

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