Senior Data Engg Analyst - MongoDB, Python, Azure, API

Optum is a global organization that delivers care, aided by technology to help millions of people live healthier lives. The work you do with our team will directly improve health outcomes by connecting people with the care, pharmacy benefits, data and resources they need to feel their best. Here, you will find a culture guided by inclusion, talented peers, comprehensive benefits and career development opportunities. Come make an impact on the communities we serve as you help us advance health optimization on a global scale. Join us to start Caring. Connecting. Growing together.

We are building enterprise-scale data and AI platforms  that power knowledge services, advanced analytics, and ML-driven decisioning across the organization.

We are looking for a Senior Data Engineer with deep expertise in MongoDB and cloud-native data architectures to design and operate highly scalable, low-latency, and secure data ecosystems. This role will focus on enabling real-time data access patterns, high-volume ingestion, and flexible schema design required for platform-scale systems supporting 30K+ concurrent users.

Primary Responsibilities:

• Enterprise Data Platform Engineering
  • Design and implement scalable, resilient data architectures using MongoDB (document model) as a core datastore
  • Build cloud-native data platforms supporting high concurrency, low latency, and multi-tenant access patterns
  • Define and enforce data modeling standards (schema design, indexing, sharding strategies) for enterprise workloads
• MongoDB-Centric Data Engineering
  • Design optimized collections, indexes, and shard keys for high-volume transactional and analytical workloads
  • Implement data access patterns for real-time applications, APIs, and AI/ML pipelines
  • Ensure high availability (replica sets), failover, and performance tuning across MongoDB clusters
  • Manage data lifecycle strategies: archiving, retention, and tiering
• Data Pipelines & Integration
  • Build and maintain real-time and batch pipelines for ingesting data into MongoDB from enterprise systems (RCM, WFM, LMS, etc.)
  • Enable event-driven architectures using Kafka/Event Hubs for streaming ingestion
  • Develop data services/APIs for downstream consumption by Infino and AMI
• AI/ML Platform Enablement (AMI)
  • Enable feature store-like capabilities using MongoDB and hybrid data layers
  • Support training and inference pipelines through curated, low-latency datasets
  • Collaborate with Data Scientists to optimize data access patterns for ML workloads
• Performance, Scalability & Reliability
  • Design for horizontal scalability (sharding, partitioning) and optimize for query performance at scale
  • Implement monitoring, observability, and alerting for platform health and SLAs
  • Drive capacity planning and cost-performance optimization
• Data Governance & Security
  • Implement enterprise-grade data governance, lineage, and auditability
  • Ensure compliance with security policies, encryption standards, and regulatory frameworks (HIPAA, etc.)
  • Define role-based access control (RBAC) and data access policies
• Cross-Platform Integration
  • Enable seamless integration across Infino (Knowledge Serve), AMI (AI/ML), and enterprise systems
  • Contribute to API-first and microservices-driven architecture
• Comply with the terms and conditions of the employment contract, company policies and procedures, and any and all directives (such as, but not limited to, transfer and/or re-assignment to different work locations, change in teams and/or work shifts, policies in regard to flexibility of work benefits and/or work environment, alternative work arrangements, and other decisions that may arise due to the changing business environment). The Company may adopt, vary or rescind these policies and directives in its absolute discretion and without any limitation (implied or otherwise) on its ability to do so

Required Qualifications:

• Bachelor's or Master's degree in Computer Science, Engineering, or related field
• 6+ years of experience in enterprise data engineering
• Core Technical Expertise
  • Deep experience with MongoDB:
    • Schema design (document modeling, embedding vs referencing)
    • Indexing strategies and query optimization
    • Sharding, replication, and performance tuning
• Solid programming skills in Python / Java / Scala
• Experience building distributed data systems and event-driven architectures
• Cloud & Platform Skills
  • Experience with Azure (preferred):
    • Cosmos DB (Mongo API), ADLS, ADF, Synapse, Databricks
  • Hands-on with streaming platforms (Kafka/Event Hubs)
  • Experience with API design and microservices integration
• Key Skills:
  • MongoDB Architecture & Performance Optimization
  • Distributed Systems & High-Scale Data Platforms
  • Real-Time Data Processing & Event Streaming
  • API-First Data Services
  • Data Governance & Security
  • Cloud-Native Engineering (Azure)

Preferred Qualifications:

• Experience designing enterprise platforms (high concurrency, multi-tenant systems)
• Exposure to AI/ML platforms, feature stores, or real-time inference systems
• Familiarity with Palantir / Databricks ecosystems
• Experience with DataOps / DevOps practices (CI/CD, Terraform, monitoring tools)

• Healthcare / RCM / WFM domain experience

At UnitedHealth Group, our mission is to help people live healthier lives and make the health system work better for everyone. We believe everyone - of every race, gender, sexuality, age, location and income - deserves the opportunity to live their healthiest life. Today, however, there are still far too many barriers to good health which are disproportionately experienced by people of color, historically marginalized groups and those with lower incomes. We are committed to mitigating our impact on the environment and enabling and delivering equitable care that addresses health disparities and improves health outcomes - an enterprise priority reflected in our mission.