Sr. Engineer, Data - Archimedes

Navitus Health Solutions, through its subsidiary Archimedes, is a leader in specialty drug management solutions. The Sr. Engineer, Data will serve as the technical lead for enterprise data engineering and architecture, focusing on building AI-ready data solutions and modernizing data platforms in a cloud-native environment.

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