Senior Data Engineer (Remote)

CareFirst•Baltimore, MD

1d•Remote

About The Position

The Senior Data Engineer designs, develops, and optimizes enterprise-scale data solutions supporting Risk Adjustment operations, regulatory reporting, and healthcare analytics. The role leads implementation of complex data pipelines, integration frameworks, and cloud-based data products using technologies such as Snowflake, Azure Data Factory (ADF), Microsoft Fabric, and Informatica IDMC/IICS, while ensuring data quality, governance, and operational reliability. Serving as a technical leader and subject matter expert, the Senior Data Engineer partners with business and technology stakeholders on Risk Adjustment programs, EDGE submissions, RADV audit readiness, diagnosis and coding accuracy initiatives, HCC processing, and trusted data products that support risk score accuracy and financial performance. The position has no direct reports but is expected to guide and mentor less experienced staff and may lead a team of matrixed resources.

Requirements

Bachelor's degree in Computer Science, Information Technology, Engineering, or a related field; alternatively, four additional years of relevant work experience may substitute for the degree.
At least 5 years of experience with database design and developing modeling tools.
Experience developing and updating ETL/ELT scripts.
Hands-on experience with application development, relational database layout and development, and data modeling.
Expert knowledge of data engineering development using SQL and Python, and extensive experience designing, building, and optimizing enterprise data solutions using Snowflake, Azure Data Factory (ADF), Microsoft Fabric, and Informatica IDMC/IICS.
Expert understanding of data architecture, data modeling, database design, and implementation concepts across cloud-based, analytical, relational, and large-scale healthcare data environments.
Expert knowledge of data integration and interoperability, including ETL/ELT frameworks, APIs, event-driven architectures, messaging technologies, and scalable data ingestion patterns.
Expert knowledge of data movement, orchestration, automation, CI/CD, Infrastructure as Code, DataOps, and operational support practices.
Expert knowledge of healthcare payer data domains, including claims, enrollment, provider, diagnosis, procedure, pharmacy, and clinical data, and how data quality and coding practices affect Risk Adjustment outcomes and financial performance.
Expert knowledge of Risk Adjustment methodologies and processes, including HCC models, diagnosis capture, coding accuracy, risk score generation, chart review programs, prospective and retrospective workflows, and related operations.
Advanced knowledge of healthcare regulatory reporting and submissions, including EDGE Server submissions, Risk Adjustment reporting, RADV audits, CMS requirements, and controls supporting compliance and audit readiness.
Expert understanding of data quality, reconciliation, validation, metadata management, data lineage, observability, and governance.
Expert technical, analytical, and problem-solving skills, including the ability to lead root-cause analysis and resolve complex issues affecting critical business and regulatory processes.
Advanced knowledge of Agile software development methodologies, DevOps/DataOps practices, and product-oriented delivery models.
Advanced ability to develop and maintain technical documentation, data specifications, source-to-target mappings, system designs, operational runbooks, and data quality controls.
Advanced written and verbal communication skills; ability to explain technical concepts, influence technical direction, communicate risks and recommendations, and collaborate with business, operational, and technology stakeholders.
Advanced ability to mentor engineers, perform code reviews, establish engineering standards, and drive adoption of best practices.
Ability to meet established deadlines and handle multiple internal and external customer service demands within expectations for service excellence.
Ability to communicate effectively and provide positive customer service, including to demanding or challenging customers.
Must be eligible to work in the U.S. without sponsorship.
Must immediately disclose any debarment, exclusion, or other event that makes the incumbent ineligible to perform work directly or indirectly on Federal health care programs.

Responsibilities

Develop and maintain infrastructure systems, including data warehouses, data lakes, and data access APIs.
Prepare and manipulate data using multiple technologies.
Interpret data, analyze results using statistical techniques, and provide ongoing reports.
Execute quantitative analyses that translate data into actionable insights.
Provide analytical and data-driven decision-making support for key projects.
Design, manage, and conduct quality control procedures for datasets from multiple systems.
Develop data models by studying existing data warehouse architecture, evaluating alternative logical data models, and applying metadata and modeling standards.
Plan data classes and subclasses, indexes, directories, repositories, messages, sharing, replication, backup, retention, and recovery.
Create data collection frameworks for structured and unstructured data.
Apply data extraction, transformation, and loading techniques to connect large datasets from varied sources.
Apply and implement best practices for data auditing, scalability, reliability, and application performance.
Guide and mentor less experienced staff and potentially lead matrixed resources.
Maintain and improve data delivery engineering knowledge through workshops, professional publications, networking, benchmarking, and professional societies.
Lead root-cause analysis and resolve complex data, integration, platform, and production support issues.
Develop and maintain technical documentation, data specifications, source-to-target mappings, system designs, operational runbooks, and data quality controls.
Perform code reviews, mentor engineers, establish engineering standards, and promote best practices for cloud data platforms, data quality, regulatory reporting, and Risk Adjustment data solutions.