Okay, I've searched for Staff or Lead Data Engineer roles in Chicago, IL, full-time, requiring skills in Python, DBT, and AWS, with 5+ years of experience, and offering performance bonuses. I have found 27 suitable positions for you.

Sure. Here's the analysis:

Job Analysis:

This Staff Data Engineer role is fundamentally about leading the design and implementation of scalable, reliable data pipelines and frameworks that empower data teams across a large, complex organization. The candidate will be a technical visionary and hands-on executor focused on modern data engineering practices, leveraging technologies like Python, Airflow, DBT, Databricks, and Snowflake within an AWS cloud environment. Success means not only delivering high-quality ETL/ELT pipelines but also driving adoption of best-practices, mentoring peers, and acting as a subject matter expert who bridges business analytics, AI, and platform engineering teams. This role expects someone comfortable navigating ambiguity inherent in evolving data ecosystems, handling batch and streaming data with a focus on automation, efficiency, and data quality management. Autonomy and strong technical judgment are critical given the need to align data solutions with broader organizational goals. The candidate will regularly face challenges such as optimizing complex workflows, ensuring data reliability in distributed systems, and integrating emerging data toolsets while enabling company-wide data initiatives. Early success likely involves delivering reusable data frameworks, streamlining data ingestion and transformation, and fostering a culture of quality and collaboration among data teams.

Company Analysis:

Motion Recruitment operates as a specialized talent and consulting firm deeply embedded in the North American IT ecosystem. Their focus on high-touch, consultative recruitment and delivery in niche technical domains like Data and Infrastructure suggests that the company values both expertise and relationship-building. While not a direct end-user company, their role as facilitators of digital transformation projects positions them as a strategic partner in technology-driven business outcomes. The culture implied is collaborative, client-focused, and innovation-minded, emphasizing deep technical skill aligned with business needs. For a Staff Data Engineer, this means working in an environment that prizes continuous learning, adaptability, and cross-functional communication. The role will likely have significant visibility, working closely across teams and possibly interfacing with key clients or partners given the consulting nature of Motion. Being part of a firm that pioneers community engagement via initiatives like Tech in Motion and the Timmy Awards points to a forward-thinking culture that values tech innovation and professional networks. Candidates who thrive here will align with the company’s mission of enabling impactful technology solutions and be comfortable in a dynamic, growth-focused setting where strategic leadership in data engineering is highly valued.

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

• Behavioral: Tell me about a time when you led a data engineering project that required coordinating multiple teams with different priorities. How did you ensure alignment and delivery?
• Behavioral: Describe a situation where you identified and resolved a critical data quality issue. What was your approach and what was the outcome?
• Behavioral: How have you mentored or educated peers in adopting new data tools or patterns, and what challenges did you face?
• Behavioral: Share an example of when you had to make a difficult technical decision without having complete information. How did you proceed?
• Technical: Walk me through how you would design a scalable ETL pipeline using DBT, Airflow, and Databricks for a complex, high-volume data source.
• Technical: How do you ensure data reliability and accuracy when building data models and mapping in Snowflake or Databricks?
• Technical: Explain your experience with CI/CD pipelines for data engineering workflows. What tools did you use and how did you implement automated testing?
• Technical: What is your approach to managing both batch and streaming ETL workloads? Can you provide an example using Airflow or similar orchestration tools?
• Situational: Imagine your data pipeline started failing intermittently in production, causing downstream analytics delays. How would you diagnose and fix the problem?
• Situational: You are asked to integrate an unstructured data source into an existing structured data pipeline supporting ML workloads. How would you approach this?
• Situational: Suppose a business analytics team requests a rushed data asset build that conflicts with your current roadmap. How would you handle the competing priorities?
• Company Fit: Why are you interested in working with Motion Recruitment and joining their Data Engineering Center of Excellence?
• Company Fit: How does your experience with cloud-based data architectures and modern engineering practices align with Motion’s consulting and digital transformation focus?
• Company Fit: Motion emphasizes community and innovation through platforms like Tech in Motion. How do you see yourself contributing to that culture?
• Questions for Interviewer: How does the Data Engineering Center of Excellence collaborate with other teams such as AI, Platform, and Business Analytics within the company?
• Questions for Interviewer: What are the immediate priorities or biggest challenges this role is expected to address within the first six months?
• Questions for Interviewer: Can you describe how Motion supports ongoing professional development and knowledge sharing specifically for data engineering professionals?