Okay, I've searched for Senior Data Engineer positions in the United States that are full-time, require 5+ years of experience, preferably remote, and list Python and Apache Airflow as key skills. I have found 142 positions for you.

Sure. Here's the analysis:

Job Analysis:

The Senior Data Engineer at CopilotIQ+Biofourmis is fundamentally hired to engineer and maintain scalable, reliable data infrastructure that powers a transformative AI-driven healthcare platform. This role is essential in ensuring high-quality, timely data availability that enables proactive and personalized in-home care for older adults with chronic conditions. Beyond writing code, the candidate must architect robust data pipelines and optimize data warehouse environments handling terabyte- to petabyte-scale data, balancing performance, cost, and governance challenges. Operating at the intersection of software engineering, data science, and healthcare analytics, they will resolve incidents rapidly while collaborating closely with cross-functional teams, including analysts and software engineers, to deliver high-impact business intelligence solutions. Success in this role means not just maintaining data flow but continuously evolving the data ecosystem by bringing in new technologies, enforcing DataOps best practices, and championing automation and security. Candidates will regularly make decisions about data modeling choices, platform optimizations, and prioritize tasks within a fast-paced environment that demands precision, agility, and strong product awareness.

Company Analysis:

CopilotIQ sits at the cutting edge of health technology focused on chronic care management for older Americans, uniquely combining connected in-home devices with AI-driven software to enable frequent, direct patient care. As a recognized leader in high-frequency connected care, the company is mission-driven toward improving long-term health outcomes, positioning itself as a transformative player in a traditionally episodic healthcare market. This growth-oriented and innovation-driven culture values collaboration, technical excellence, and a holistic view of healthcare delivery — all critical for a data engineer who must seamlessly integrate technical solutions with frontline clinical needs. Working remotely as an individual contributor, the Senior Data Engineer role likely has high visibility, partnering with clinical and product teams to directly influence platform capabilities. The company's commitment to advanced analytics and scalable infrastructure signals ongoing investments in tech innovation, providing the candidate with opportunities to lead technical initiatives that impact millions of lives. Overall, the role is deeply strategic, empowering a data engineer to drive measurable improvements in accessible healthcare within a company that prioritizes impact, reliability, and continuous learning.

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

• Behavioral: Tell me about a time you identified and resolved a critical issue in a production data pipeline. How did you approach the problem and what was the outcome?
• Behavioral: Describe an instance where you had to collaborate with cross-functional stakeholders (e.g., analysts, software engineers) to deliver a complex data solution. How did you ensure alignment and success?
• Behavioral: Give an example of how you introduced automation or DataOps practices (such as CI/CD or data quality tests) to improve data engineering workflows in a previous role.
• Behavioral: How do you prioritize and manage your workload in a fast-paced environment with competing deadlines and evolving requirements?
• Technical: Walk me through your experience designing scalable data pipelines using Airflow (or similar orchestration tools). How do you ensure reliability and performance?
• Technical: How have you optimized data warehouse query performance and infrastructure costs at scale? Can you provide specific techniques or tools you used?
• Technical: Explain your approach to data modeling in event-driven or dimensional schema architectures. Why are these models important in large health tech datasets?
• Technical: Describe your experience with streaming data technologies like Kinesis or Kafka. How do you handle real-time data ingestion and processing challenges?
• Situational: Imagine a key data pipeline fails overnight and business dashboards are impacted. How would you diagnose and remediate the issue under pressure?
• Situational: Suppose you’re asked to introduce a new data platform technology (e.g., Redshift Serverless) to improve scalability and cost efficiency. How would you evaluate and implement this change?
• Situational: You notice discrepancies in data quality that impact reporting accuracy. What steps do you take to identify root causes and prevent future issues?
• Company Fit: What excites you about CopilotIQ’s mission to transform in-home care for seniors, and how do you see your data engineering skills contributing to this goal?
• Company Fit: How do you align with a fast-paced, innovation-driven environment that balances technical rigor with a mission to improve healthcare outcomes?
• Company Fit: Given CopilotIQ’s emphasis on collaboration across clinical, product, and engineering teams, how do you ensure your work supports cross-functional goals effectively?
• Questions to Ask Interviewer: How does the data engineering team currently collaborate with clinical and product leadership to prioritize new data initiatives?
• Questions to Ask Interviewer: What emerging challenges or opportunities is CopilotIQ anticipating in scaling its data infrastructure over the next 12 months?
• Questions to Ask Interviewer: Can you describe the company culture and how it supports continuous learning and innovation within the engineering team?