Senior Analyst, Data Engineering

The Data & Analytics Function oversees the analytics lifecycle to identify, analyze, and present actionable insights that drive business decisions and create competitive advantage. This function manages enterprise data platforms, data access, governance, reporting, business intelligence solutions, and advanced analytics capabilities.

Data Engineering is responsible for building, maintaining, and optimizing scalable data platforms that enable efficient data ingestion, transformation, orchestration, and consumption across the enterprise. As part of the Platform Engineering team, this role focuses on delivering reliable, secure, automated, and scalable data infrastructure supporting business-critical analytics and data products.

The ideal candidate will contribute to platform operations, cloud-based data engineering solutions, CI/CD automation, and production support while partnering with cross-functional teams to ensure a stable and high-performing data ecosystem.

• 3-5 years of experience in Data Engineering, Platform Engineering, Site Reliability Engineering (SRE), DevOps, or related technical roles.
• Bachelor’s degree in computer science, Engineering, Information Systems, or a related discipline (or equivalent work experience).
• Hands-on experience with Databricks, Apache Spark, and cloud-based data platforms.
• Experience developing, maintaining, and supporting CI/CD pipelines using Harness, Azure DevOps, GitHub Actions, or similar tools.
• Working knowledge of cloud platforms such as Microsoft Azure, Google Cloud Platform (GCP), or AWS.
• Experience with SQL, Python, PySpark, and data engineering best practices.
• Familiarity with source control tools such as Git/GitHub.
• Basic understanding of Infrastructure as Code (IaC) concepts, preferably Terraform.
• Knowledge of platform monitoring, observability, incident management, and operational support processes.
• Understanding of data governance, security, access management, and platform reliability principles.

• →Support the design, implementation, and maintenance of enterprise data platform solutions.
• →Develop, deploy, and manage data processing workloads using Databricks and Apache Spark.
• →Build and maintain CI/CD deployment pipelines using Harness and other DevOps tools.
• →Support platform modernization, cloud migration, automation, and continuous improvement initiatives.
• →Monitor platform health, performance, and availability and proactively identify opportunities for optimization.
• →Investigate and resolve platform issues, deployment failures, and data pipeline incidents.
• →Collaborate with Data Engineers, Architects, Product Teams, and Business Stakeholders to deliver reliable data platform capabilities.
• →Ensure adherence to security, compliance, governance, and operational standards.
• →Participate in production support activities, root cause analysis, and incident resolution processes.
• →Contribute to documentation, operational runbooks, and knowledge-sharing initiatives.
• →Work closely with global teams to deliver highly available and scalable platform services.

• Applies foundational technical knowledge and engineering best practices to platform and data engineering tasks.
• Works on projects of moderate scope under guidance from senior engineers and technical leads.
• Troubleshoots and resolves technical issues using established procedures and operational frameworks.
• Demonstrates strong analytical and problem-solving capabilities.
• Continuously develops expertise in Databricks, cloud technologies, DevOps, and platform engineering practices.
• Supports platform reliability, automation, and operational excellence initiatives.
• Effectively collaborates within cross-functional and geographically distributed teams.
• Takes ownership of assigned deliverables and contributes to team success.

• Databricks (Workflows, Delta Lake, Unity Catalog)
• Apache Spark / PySpark
• Harness CI/CD Pipelines
• Python
• SQL
• Azure Data Platform, GCP, or AWS
• Git/GitHub
• Terraform
• Monitoring and Observability Tools
• DevOps & Platform Engineering Practices
• Incident and Production Support Management

This position is part of a global Data Engineering Platform Operations team and requires participation in a rotational shift model to provide support coverage across multiple regions and time zones.

Candidates should be comfortable working in the following rotational shifts based on business needs:

• Morning Shift: 6:00 AM - 3:00 PM
• Afternoon Shift: 1:00 PM - 10:00 PM
• Evening Shift: 5:30 PM - 2:30 AM

• Participate in rotational production support and platform operations activities.
• Monitor and support Databricks environments, data pipelines, CI/CD deployments, and platform services.
• Respond to platform incidents, service disruptions, deployment failures, and operational requests within defined SLAs.
• Collaborate effectively with globally distributed platforms, engineering, and business teams.
• Support continuous improvement initiatives focused on automation, reliability, and operational efficiency.

This role offers an excellent opportunity to gain hands-on experience with modern data platforms, cloud technologies, Databricks, DevOps practices, and enterprise-scale data engineering operations.

Cardinal Health supports an inclusive workplace that values diversity of thought, experience and background. We celebrate the power of our differences to create better solutions for our customers by ensuring employees can be their authentic selves each day. Cardinal Health is an Equal Opportunity employer. All qualified applicants will receive consideration for employment without regard to race, religion, color, national origin, ancestry, age, physical or mental disability, sex, sexual orientation, gender identity/expression, pregnancy, veteran status, marital status, creed, status with regard to public assistance, genetic status or any other status protected by federal, state or local law.

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