Okay, I've searched for Data Engineer roles in Tallahassee, FL within the healthcare industry, requiring 4-6 years of experience, full-time schedule, and skills in Google Cloud Platform technologies. I have found 37 positions for you.

Sure. Here's the analysis:

Job Analysis:

This Data Engineer role at Cardinal Health, specifically within the Navista oncology-focused division, centers on building and operationalizing large-scale, cloud-based data platforms primarily on Google Cloud Platform (GCP). The core purpose is to enable advanced analytics and machine learning initiatives that empower oncology practices with actionable, data-driven insights. Beyond just coding and pipeline construction, the role entails technical leadership — mentoring peers, optimizing performance and costs, and embedding data governance to ensure quality and security of highly sensitive healthcare data. Success here means reliably delivering scalable, maintainable data infrastructure that integrates diverse clinical and operational datasets in a regulated environment, enabling seamless collaboration among data scientists, analysts, and product teams. The candidate must tackle complex problems such as handling large oncology data lakes, ensuring compliance with data governance, and supporting agile DevSecOps practices, all while balancing innovation with robust delivery.

Company Analysis:

Cardinal Health stands as a mature, Fortune 500 healthcare giant providing critical supply chain and clinical solutions globally, with nearly a century of domain expertise. The Navista unit reflects a strategic growth area focused on patient-centered oncology care, blending clinical insight with technology innovation. This environment is likely mission-driven yet complex, with deep cross-functional collaboration among clinical, data, and IT teams. There is an emphasis on inclusivity, diversity, and autonomy balanced with alignment to tight regulatory standards and business goals. As a large, established company, the culture may value process, rigor, and stability, but within Navista, there's noticeable drive toward agility and cutting-edge analytics to disrupt the oncology space. The role’s strategic importance is high, directly supporting the company’s digital health vision and data-centric transformation to improve cancer care delivery.

Absolutely. Here are some mock interview questions that could come up:

• Behavioral: Tell me about a time when you had to mentor junior engineers while managing a complex data engineering project. How did you balance leadership with hands-on work?
• Behavioral: Describe an instance where you faced conflicting priorities between data quality/security and delivery speed. How did you resolve the conflict?
• Behavioral: Share an experience where you collaborated closely with data scientists or product teams to translate business or clinical needs into scalable data solutions.
• Behavioral: Have you ever helped implement data governance policies in a healthcare or regulated environment? What challenges did you face and how did you overcome them?
• Technical: Walk me through how you would design and implement a cost-effective, scalable data lake on Google Cloud Platform optimized for oncology data analytics.
• Technical: What strategies do you use to optimize complex SQL queries and data pipelines for performance and cost efficiency in a cloud environment?
• Technical: How do you approach integrating multiple data ingestion technologies like Cloud Dataflow, Pub/Sub, and Airflow to build automated, low-code/no-code pipelines?
• Technical: Explain your experience with operationalizing DataOps or MLOps practices. How have these improved workflow velocity and solution reliability?
• Situational: Imagine you find discrepancies between data sets being consumed by data scientists and receiving complaints about pipeline latency. How would you investigate and address these issues?
• Situational: If required to lead a migration of existing on-prem data infrastructure to GCP while ensuring zero disruption to clinical analytics, how would you plan and execute this?
• Situational: In a scenario where regulatory audits find gaps in data governance compliance within your data platform, what immediate and long-term actions would you take?
• Company Fit/Motivation: Why are you interested in working with Cardinal Health’s Navista team particularly in oncology data engineering?
• Company Fit/Motivation: How do you see your personal values aligning with Cardinal Health’s commitment to diversity, inclusion, and patient-centered care?
• Company Fit/Motivation: What excites you most about enabling machine learning and cloud analytics in healthcare, and how do you stay current on emerging data engineering technologies?
• Candidate Questions: Can you describe the data team structure within Navista and how collaboration typically happens with clinical stakeholders?
• Candidate Questions: How does Cardinal Health approach innovation within a large, regulated environment, particularly in the context of advanced analytics and AI initiatives?
• Candidate Questions: What are the immediate data engineering priorities for Navista in the next 6 to 12 months, and how will this role contribute to those goals?