Sr. Data Analyst - Optum Financial - Remote

Primary Responsibilities

• Design, build, and maintain data pipelines and workflows using Databricks (SQL, PySpark)
• Develop and manage automated file ingestion and transformation processes, including scheduled and event driven file automation
• Design and build curated datasets that power downstream reporting and analytics, ensuring data integrity, consistency, and usability across stakeholder needs
• Maintain and optimize production data pipelines and reporting jobs, including monitoring, troubleshooting, and ensuring reliable end to end data delivery
• Implement and support CI/CD pipelines for data assets using version control and automated deployment practices
• Collaborate with cross-functional teams to understand data requirements and translate them into scalable technical solutions
• Monitor, troubleshoot, and optimize data pipelines for performance, reliability, and cost efficiency
• Ensure data quality, validation, documentation, and adherence to enterprise data governance standards
• Support analytics and reporting teams by enabling trusted data sources for downstream tools (e.g., Power BI)
• Contribute to continuous improvement of data engineering and analytics best practices

Required Qualifications

• 3+ years of experience in data analytics, data engineering, or a related role
• Solid hands-on experience with Databricks, including Databricks SQL and PySpark
• Solid SQL skills and experience working with large, structured datasets
• Experience building and maintaining data pipelines, datasets, and automated workflows
• Working knowledge of CI/CD practices and version control systems (e.g., Git)
• Proven ability to independently analyze problems, design solutions, and deliver results
• Proven solid communication skills and ability to collaborate with both technical and non-technical stakeholders

Preferred Qualifications

• Experience working in cloud-based data platforms and enterprise data lake or lakehouse architectures
• Experience supporting analytics or BI tools (e.g., Power BI)
• Experience with file-based data ingestion (e.g., CSV, JSON, Parquet) and automation frameworks
• Experience in an enterprise or financial services environment
• Exposure to Agile or DevOps delivery models
• Familiarity with data governance concepts such as metadata, lineage, and access controls