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