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.
Primary Responsibilities:
- Design, build, and optimize data ingestion and transformation pipelines on Azure Databricks (Delta Lake/Spark) with an eye for performance, cost, and operational simplicity
- Implement streaming and batch patterns leverage CDC, eventing (Service Bus/Event Grid), and orchestration (Azure Data Factory, Functions) to move and process data reliably
- Engineer highquality PySpark code and SQL, enforce coding standards, and enable reuse via libraries and notebooks
- Build/extend APIs and microservices that expose curated data for realtime and nearrealtime use cases ensure backward compatibility and observability
- Contribute to CI/CD (Azure Pipelines or equivalent), infraascode, secrets management (Key Vault), and automated testing for data and services
- Partner with architects/SMEs to migrate mainframe workloads (batch & inquiry APIs) to cloudnative paradigms with clear SLAs and error budgets
- Instrument solutions with endtoend monitoring/alerting drive MTTR down through robust runbooks, dashboards, and noisefree alerts
- Collaborate with business & platform teams to onboard new consumers, model data contracts, and scale for rapid growth in volume and users
- Contribute to data governance (catalog, lineage, access controls/Unity Catalog) and ensure compliance with enterprise standards
- Participate in oncall/production support rotations to maintain 247 reliability and SLA adherence perform rootcause analysis and preventive hardening
- Document designs, patterns, and operational guides that enable fast onboarding and consistent delivery across teams
- 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 regards 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 degree in Software Engineering with having relevant experience from 3+ years
- Experience building eventdriven and batch data flows using Azure Data Factory, Service Bus/Event Grid, and Azure Functions
- Handson Azure Databricks (Spark, Delta Lake) with solid PySpark and SQL skills for largescale data processing
- API engineering (REST/gRPC), contract versioning, and service observability (logs, metrics, traces)
- CI/CD exposure on Azure (Pipelines/GitHub), including test automation and deployment strategies for data & services
- Familiarity with secrets & access via Azure Key Vault, and rolebased access in data platforms (Unity Catalog/ACLs)
- Proven ability to design for reliability (idempotency, retries, transactional writes, schema evolution) and to operate at scale (performance & cost tuning).
- Proven production mindset: oncall readiness, incident response, and proactive postincident improvements to protect SLAs for a large consumer base
Preferred Qualifications:
- Experience migrating mainframe data and workloads to cloud healthcare claims domain exposure
- Experience with Azure DevOps governance and enterprise templates for JDs (tone/structure)
- Delta Live Tables, Structured Streaming, or Kafka ingestion patterns
- Data quality frameworks (expectations, unit tests), and lineage/catalog tools
- Proven performance engineering for Spark (partitioning, caching, joins, skew mitigation) and SQL Warehouse optimization
- Proven secure engineering practices (PII handling, encryption at rest/in transit, network isolation). [Alex Overv...or Enduser | Word]
