Data Engineering

Data Analyst 

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:

• Tech Stack:Azure, Databricks, Power BI, and PostgreSQL
• Responsibilities Across the Data Lifecycle
• Discovery & Ingestion
  • Source assessment, contracts, SLAs; landing design (batch/stream)
  • Build resilient ingestion with lineage and schema change alerts
• Storage & Modeling
  • Define bronze/silver/gold zones; dimensional models for BI; CDC into Delta/Synapse
  • Govern via Unity Catalog/Purview; metadata completeness ≥95%
• Transformation & Quality
  • Implement SDE/SLAs for transformations; automated data quality checks (completeness, accuracy, timeliness)
  • Versioned pipelines, testing (unit/integration), rollback plans, blue/green deployments
• Serving & Visualization
  • Curated datasets with documented measures; self-service enablement via Power BI; certified datasets
  • Performance SLAs (e.g., P95 < 3s report load; streaming lag < 60s)
• Operations & Security
  • Observability (alerts, SLOs, error budgets), incident management, change management
  • Least privilege access, key rotation, secret scanning, compliance (e.g., GDPR-like PII controls)
• Cost & Efficiency
  • Monitors spend per pipeline/workspace; enforce cluster policies; storage lifecycle tiering
  • Right-size jobs and warehouse pools; commit/reserved capacity were ROI-positive
• 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 a related field
• 7+ Years of experience in the Data Engineering Space with the below Skills and Responsibilities
• Ability to design and operate Azure + Databricks Lakehouse with bronze/silver/gold architecture, governed by Unity Catalog and Purview
• Ability to build batch and streaming pipelines (ADF, Structured Streaming), with automated data quality checks, CI/CD, and observability
• Ability to model curated datasets and semantic layers in Power BI; implement RLS/OLS and deployment pipelines for enterprise-scale reporting
• Ability to manage PostgreSQL schemas, performance, and replication strategies; implement CDC to Lakehouse/Synapse
• Ability to enforce security baselines (Managed Identity, Private Endpoints, Key Vault) and compliance for sensitive data
• Ability to drive cost optimization via cluster policies, autoscaling, storage tiering, and reserved capacity planning.
• Ability to collaborate with product owners and data stewards to standardize metric definitions and certify datasets

 

Recommended Certifications & Learning Paths:

• Microsoft Azure:
  • DP-203 (Data Engineering on Microsoft Azure)
  • AZ-305 (Architecting) for cross-cloud design
• Databricks:
  • Data Engineer Associate/Professional
  • Lakehouse Fundamentals
• Power BI:
  • PL-300 (Power BI Data Analyst)
• PostgreSQL:
  • EDB or Crunchy Data PostgreSQL certifications (optional but valuable)