Sr. Engineer, Data - Archimedes

Navitus Health Solutions is a leader in specialty drug management solutions, aiming to transform the PBM industry. The Sr. Engineer, Data is responsible for leading enterprise data engineering and architecture initiatives, focusing on modernizing data platforms and ensuring data governance to support analytics and AI initiatives.

Responsibilities:

• Serve as the technical lead for enterprise data engineering, lakehouse architecture, data modeling, and data platform modernization initiatives
• Design and implement lake house architectures utilizing Azure Databricks, Delta Lake, Azure Data Lake Storage Gen2, Unity Catalog, and related modern data platform technologies
• Develop canonical enterprise data models, business entity mappings, master data structures, and reusable data products supporting enterprise reporting, analytics, automation, and AI initiatives
• Build and maintain bronze, silver, and gold data layers supporting governed, trusted, and AI-ready datasets
• Develop and maintain metadata, data dictionaries, business glossaries, lineage documentation, and data catalog integrations supporting enterprise data governance
• Design, develop, and maintain ETL pipelines for structured and unstructured data across cloud and on-prem environments
• Build and optimize data models, schemas, and storage solutions in SQL Server, PostgreSQL, and cloud-native databases
• Design and deliver AI-ready data products supporting machine learning, generative AI, intelligent automation, retrieval-augmented generation (RAG), and agent-based AI solutions
• Support feature engineering, vectorization pipelines, document enrichment, semantic search, and knowledge management capabilities used by enterprise AI platforms
• Design and support healthcare data integration patterns including claims, eligibility, pharmacy, clinical, operational, financial, and third-party partner data
• Define and maintain enterprise data architecture standards, canonical data models, integration patterns, naming conventions, and data governance frameworks
• Lead enterprise data modeling efforts across operational, analytical, and AI workloads, ensuring consistent business definitions and semantic alignment across platforms
• Establish and govern enterprise data dictionaries, business glossaries, metadata standards, data lineage frameworks, and data stewardship practices
• Provide technical leadership and mentorship to Data Engineers, Data Integration Engineers, Analytics Engineers, and related technical resources
• Conduct architecture reviews for data platforms, integrations, analytics solutions, AI initiatives, and modernization programs
• Design enterprise data products and reusable domain-oriented datasets supporting reporting, analytics, automation, machine learning, and generative AI use cases
• Define reference architectures for healthcare data integration, interoperability, operational reporting, AI-ready datasets, and governed analytical platforms
• Support ingestion and transformation of structured, semi-structured, and unstructured healthcare datasets from internal and external sources
• Implement CI/CD workflows for data pipeline deployment and monitoring using tools such as GitHub Actions, Azure DevOps, or Jenkins
• Develop and maintain data integrations using AWS Glue, Azure Data Factory, Lambda, EventBridge, and other cloud-native services
• Design and implement DataOps practices including automated testing, deployment automation, data quality validation, monitoring, observability, and CI/CD pipelines for data workloads
• Develop API-based integrations supporting SaaS platforms, operational applications, third-party systems, healthcare data exchanges, intelligent automation platforms, and enterprise workflows
• Design and support event-driven architectures utilizing Event Hub, Event Grid, Service Bus, APIs, webhooks, and streaming data technologies
• Support machine learning, artificial intelligence, predictive analytics, and intelligent automation initiatives by developing scalable data pipelines, feature engineering datasets, training datasets, and operationalized data products
• Partner with RPA, automation, analytics, and AI teams to support workflow automation, intelligent document processing, agent-based AI solutions, and enterprise automation initiatives
• Implement secure data engineering practices including encryption, RBAC, data masking, row-level security, auditing, lineage tracking, and governance controls
• Ensure data quality, lineage, and governance through automated validation, logging, and monitoring frameworks
• Collaborate with cross-functional teams to gather requirements, design scalable solutions, and support analytics and reporting needs
• Monitor and troubleshoot data pipeline performance, latency, and failures; implement proactive alerting and remediation strategies
• Support data security and compliance by enforcing access controls, encryption standards, and audit logging aligned with HIPAA and SOC 2
• Maintain documentation for data flows, architecture diagrams, and operational procedures
• Participate in sprint planning, code reviews, and agile ceremonies to support iterative development and continuous improvement
• Evaluate and integrate new data tools, frameworks, and cloud services to enhance platform capabilities
• Partner with DevOps and Security teams to ensure infrastructure-as-code and secure deployment practices are followed
• Participate in, adhere to, and support compliance, people and culture, and learning programs
• Perform other duties as assigned

Requirements:

• Bachelor's degree in Computer Science, Information Systems, Data Engineering, or related field required
• AWS Certified Data Analytics or Solutions Architect, Microsoft Certified: Azure Data Engineer Associate, and Certified Data Management Professional (CDMP) required
• 8+ years of experience in Data Engineering, Data Architecture, Analytics Engineering, Data Platform Engineering, or related disciplines required
• 5+ years of experience designing and implementing modern lake house architectures utilizing Azure Databricks, Delta Lake, Azure Data Lake Storage Gen2, Unity Catalog, and related cloud-native data technologies required
• Demonstrated experience leading enterprise data architecture, canonical data modeling, data governance, master data management, and large-scale data modernization initiatives required
• Experience designing enterprise data products, semantic models, business entity mappings, data dictionaries, and governed analytical datasets required
• Strong experience with Apache Spark, PySpark, SQL, Python, DataOps automation, CI/CD pipelines, and cloud-native data engineering practices required
• Experience supporting and modernizing legacy SQL-based ETL, reporting, data warehouse, and operational data environments required
• Master's degree preferred
• Experience supporting machine learning, AI, generative AI, intelligent automation, retrieval-augmented generation (RAG), vector-based architectures, and AI-ready data platforms preferred
• Experience planning and executing migrations from traditional database-centric architectures to cloud-native lakehouse, analytics, and AI platforms preferred
• Experience rationalizing legacy data assets, consolidating data pipelines, and establishing enterprise data architecture standards preferred
• Experience supporting healthcare data domains including claims, eligibility, pharmacy, clinical, operational, provider, financial, and regulatory data preferred
• Experience mentoring engineers, conducting architecture reviews, establishing engineering standards, and providing technical leadership across cross-functional teams preferred
• Knowledge of modern data architecture patterns including Data Mesh, Data Products, Medallion Architecture, Master Data Management, Event-Driven Architecture, and Lakehouse Governance preferred
• Experience developing canonical data models, enterprise data products, data mappings, master data structures, and governed analytical datasets preferred
• Experience building DataOps pipelines, automated testing frameworks, CI/CD processes, and data quality controls preferred
• Experience supporting AI, machine learning, analytics, automation, and intelligent business solutions through scalable data engineering practices preferred
• Experience working within regulated environments supporting HIPAA, HITRUST, SOC 2, NIST, or similar compliance frameworks preferred