Okay, I've searched for Data Engineer full-time roles in Atlanta, GA within the AI and Industrial IoT industry, requiring 4+ years of experience and PySpark skills. I have found 72 positions for you.

Sure. Here's the analysis:

Job Analysis:

This Data Engineer I role at Honeywell is fundamentally about building and maintaining scalable, robust data infrastructure to power advanced AI and Industrial IoT solutions. The candidate will be tasked with designing end-to-end data pipelines that handle high-volume real-time telemetry data from IoT devices, preparing this data to fuel AI/ML models including Large Language Models (LLMs) and other autonomous AI systems. The emphasis on RAG (Retrieval Augmented Generation) and vector databases indicates a focus on cutting-edge AI data retrieval techniques, reflecting the company's commitment to innovation. Success in this role hinges not only on technical excellence in distributed data processing (using PySpark/Scala and cloud platforms like Azure/GCP/Databricks) but also on strong DataOps capabilities to ensure continuous delivery, quality, and observability of data products. The candidate must thrive under evolving, agile project conditions—navigating ambiguities with practical solutions that directly impact industrial AI applications. Interdisciplinary collaboration is critical here: the engineer will partner closely with data scientists and ML engineers to translate complex industrial datasets into actionable AI models, while also upholding data governance, security, and scalability standards. In essence, one must be both a skilled coder and an innovative thinker who can balance speed, quality, and collaboration to drive Honeywell's industrial AI vision forward.

Company Analysis:

Honeywell operates as a global leader in industrial technology, with a strong foothold in AI-driven industrial solutions—a space demanding both reliability and cutting-edge innovation. The company’s positioning as a mature but forward-thinking player means this role offers a unique blend of stability and high-growth opportunity. Given Honeywell's emphasis on IoT and AI, the culture likely values precision, innovation, and cross-disciplinary teamwork to solve complex industrial challenges. Candidates can expect a professional environment that combines structured engineering processes with agile methodologies, encouraging continuous improvement and creative problem-solving. The company’s offerings of hybrid work schedules and comprehensive benefits suggest a respect for work-life balance and employee well-being, which supports sustainable performance in challenging technical roles. This role will likely sit within a larger, possibly global engineering team, requiring the ability to collaborate across time zones and functions. Strategically, this hire supports Honeywell’s broader AI and industrial automation objectives—helping scale and modernize AI data infrastructure to maintain market leadership and unlock new operational efficiencies for industrial customers. The candidate who aligns with Honeywell’s mission to ‘make the future’ by integrating AI and industrial scale will find this role both impactful and growth-oriented.

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

• Behavioral: Tell me about a time when you had to design a data pipeline for real-time streaming data. What challenges did you face and how did you overcome them?
• Behavioral: Describe a situation where you had to collaborate closely with data scientists or ML engineers. How did you ensure smooth communication and successful delivery?
• Behavioral: Can you share an experience where you identified data quality issues that impacted AI model performance? What steps did you take to resolve it?
• Behavioral: How do you handle working under shifting requirements in a fast-paced, agile environment?
• Technical: Walk me through your experience building ETL/ELT workflows, particularly using PySpark or Scala. How do you optimize for performance and scalability?
• Technical: Explain how you would architect a data solution to support a Retrieval Augmented Generation (RAG) system involving vector databases and embedding pipelines.
• Technical: What are best practices for implementing DataOps in AI/ML data pipelines? How have you applied these in past projects?
• Technical: Describe your experience with real-time data processing frameworks like Apache Spark Streaming or Structured Streaming.
• Situational: Imagine your data pipeline is experiencing significant latency impacting real-time AI inference. How would you diagnose and mitigate this issue?
• Situational: You discover that the data sources from IoT devices are inconsistent and occasionally missing critical telemetry. How would you design your pipeline to handle this?
• Situational: Your team disagrees on the data modeling approach for a complex AI workload. How do you navigate this conflict to reach a consensus?
• Company Fit: Why are you interested in working at Honeywell, especially in the context of AI and Industrial IoT?
• Company Fit: Honeywell emphasizes collaboration between disciplines like data science and engineering. How do your values and work style align with this approach?
• Company Fit: Honeywell is leveraging AI for industrial customers to 'make the future.' How do you see your role contributing to this mission?
• Questions for Interviewer: How does Honeywell’s AI and data engineering team stay ahead of rapidly evolving AI technologies and industrial requirements?
• Questions for Interviewer: Can you describe how cross-functional collaboration works day-to-day between data engineering and ML teams?
• Questions for Interviewer: What are the key success metrics you use to evaluate the effectiveness of data engineers in this role?