Job Summary
Sr Developer with ten to fourteen years of experience will design build and optimize data engineering solutions using Databricks Apache Spark and Palantir Foundry for complex Medicare and Medicaid claims processing in a hybrid work model. The role enables reliable analytics for care quality compliance and cost optimization in a day shift setting without travel requirements.
Responsibilities
- Design scalable data pipelines on Databricks to ingest transform and curate Medicare and Medicaid claims data so that downstream analytics and reporting systems operate with trusted and timely information
- Develop high performance Apache Spark jobs to process large volumes of structured and semi structured healthcare data and ensure that batch and near real time workloads meet strict service level objectives
- Build robust data models and assets in Palantir Foundry to support claims adjudication analysis payment integrity reviews and utilization management while aligning to enterprise data governance practices
- Optimize Spark and Databricks workloads by tuning storage formats cluster configurations and execution plans to reduce compute costs and improve throughput for critical actuarial and operational dashboards
- Implement data quality rules validation frameworks and automated monitoring for claims data to detect anomalies ensure regulatory compliance and provide reliable inputs for clinical and financial decision making
- Collaborate with product owners business analysts and claims operations teams to translate complex Medicare and Medicaid policy requirements into clear technical specifications and reusable data components
- Create reusable data engineering patterns and documentation for ingestion transformation and publishing layers so that other developers can rapidly extend capabilities across new claims programs and reporting initiatives
- Integrate security and privacy controls into all data solutions including role based access control encryption and audit logging to protect member information and support organizational compliance with healthcare regulations
- Coordinate with cloud infrastructure and platform teams to plan capacity manage environments and ensure that Databricks Apache Spark and Foundry solutions remain stable resilient and aligned with enterprise architecture standards
- Automate testing deployment and release processes using continuous integration and continuous delivery practices so that data products for claims analytics can be promoted across environments with minimal risk and downtime
- Provide advanced troubleshooting and root cause analysis for data incidents affecting claims feeds metrics or regulatory reports and implement corrective actions that prevent recurrence and strengthen platform reliability
- Guide junior developers through code reviews design discussions and knowledge sharing sessions focused on data engineering best practices in Databricks Spark and Foundry ensuring consistent delivery quality across the team
- Engage with stakeholders to present solution options explain technical tradeoffs and gather feedback so that data products for Medicare and Medicaid claims remain relevant useful and aligned with business objectives
Qualifications
- Demonstrate deep hands on expertise in Databricks including notebooks jobs clusters and workspace management to deliver complex data engineering solutions for healthcare claims processing
- Apply advanced Apache Spark knowledge in batch and streaming scenarios covering performance tuning data partitioning and error handling to support large scale Medicare and Medicaid analytics
- Use strong proficiency with Palantir Foundry including ontology design pipelines and data set management to create reliable analytical assets for claims operations and payment integrity functions
- Exhibit thorough understanding of Medicare and Medicaid claims structures coding guidelines and adjudication workflows to ensure that data models and transformations accurately reflect business realities
- Leverage solid experience in SQL and data warehousing concepts to design clear semantic layers and reporting views that simplify access to claims metrics for analytics and business users
- Utilize practical experience with cloud based data platforms and related ecosystem tools to integrate Databricks Spark and Foundry solutions into broader enterprise architectures for healthcare organizations
- Apply good knowledge of version control practices testing strategies and continuous integration pipelines so that data engineering artifacts remain maintainable traceable and easy to evolve over time
Über Cognizant
Cognizant (NASDAQ: CTSH) i ist ein Technologiedienstleister und Entwickler von KI-Lösungen. Wir schlagen die Brücke zwischen KI-Investitionen und echtem unternehmerischem Mehrwert, indem wir ganzheitliche Full-Stack-KI-Lösungen für unsere Kunden entwickeln. Mit unserer fundierten Branchen-, Prozess- und Engineering-Expertise integrieren wir die spezifischen Anforderungen von Unternehmen passgenau in Technologiesysteme. So entfalten wir das menschliche Potenzial, erzielen greifbare Ergebnisse und sichern globalen Unternehmen in einer sich rasant wandelnden Welt den entscheidenden Vorsprung. Erfahren Sie mehr unter cognizant.ai oder @cognizant.
Zusätzliche Informationen zur Beschäftigung
Die Vergütungsinformationen sind zum Zeitpunkt der Veröffentlichung dieser Stellenausschreibung korrekt. Cognizant behält sich das Recht vor, diese Informationen jederzeit unter Beachtung der geltenden gesetzlichen Bestimmungen zu ändern.
Bewerberinnen und Bewerber können verpflichtet sein, an Vorstellungsgesprächen persönlich oder per Videokonferenz teilzunehmen. Darüber hinaus kann es erforderlich sein, bei jedem Gespräch einen gültigen staatlichen Lichtbildausweis vorzulegen.
Cognizant ist ein Arbeitgeber mit Chancengleichheit. Ihre Bewerbung und Kandidatur werden nicht aufgrund von Rasse, Hautfarbe, Geschlecht, Religion, Glaubensbekenntnis, sexueller Orientierung, Geschlechtsidentität, nationaler Herkunft, Behinderung, genetischen Informationen, Schwangerschaft, Veteranenstatus oder sonstiger durch bundes‑, landes‑ oder kommunalrechtliche Vorschriften geschützter Merkmale berücksichtigt oder abgelehnt.
