Okay, I've searched for Golang Software Engineer roles in Chicago, IL, onsite, requiring 5-7 years of experience and strong skills in AWS and Apache Spark. I have found 53 positions for you.

Sure. Here's the analysis:

Job Analysis:

This Golang Software Engineer role at FreeWheel, a Comcast company, is fundamentally centered on architecting and maintaining large-scale, high-performance data pipelines and infrastructures that underpin advanced advertising platforms. The candidate is expected not only to code proficiently in Golang and Python (or similar language like Scala) but to deeply understand and work with modern cloud-native architectures—particularly AWS services, Kubernetes orchestration, and containerization. The core challenge lies in managing complex, real-time, and large-volume data flows across distributed systems while ensuring reliability, scalability, security, and cost-efficiency. Success means the engineer can translate ambiguous business needs into robust technical solutions that enable rapid growth, support cross-functional teams, and actively improve system metrics such as pipeline throughput, latency, and platform stability. The emphasis on collaboration, documentation, and staying abreast of technological trends suggests the role requires a proactive mindset, strong communication, and continuous learning. In essence, this position balances hands-on technical expertise with strategic thinking about data infrastructure in an evolving ad-tech environment, where high data volumes and fast iteration cycles are the norm.

Company Analysis:

FreeWheel operates as a global leader in advanced advertising technology within the highly competitive ad-tech industry, backed by the strength and resources of Comcast. Its positioning as an innovation-driven, technology-forward company means this software engineering role will exist at the intersection of cutting-edge data science, cloud computing, and real-time media monetization. The culture at FreeWheel seems to prioritize teamwork, inclusivity, and customer-centric innovation, supported by a framework that encourages continuous feedback and agile methodologies. For a candidate, this implies working in a dynamic, fast-paced setting with high visibility and cross-team interaction, where adaptability and collaborative problem-solving are critical. Being part of a high-impact data engineering team focused on global-scale challenges further signals the opportunity to influence the company’s strategic growth, especially in areas like Unified Data Lake infrastructure and novel advertising products like Universal Ads. The company's stated values—such as driving results, respecting diversity, and owning the customer experience—hint at a workplace culture that rewards accountability, passion, and diverse perspectives, making it an ideal environment for those who aim to grow technically while contributing meaningfully to business outcomes.

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

• Behavioral: Tell me about a time when you had to debug and resolve a complex data pipeline issue under pressure. How did you approach it and what was the outcome?
• Behavioral: Describe a situation where you collaborated with cross-functional teams with differing technical backgrounds. How did you ensure effective communication and project progress?
• Behavioral: Can you share an example of adaptation in your career when priorities or technologies shifted rapidly? How did you maintain productivity and stay motivated?
• Behavioral: Discuss a time you identified an opportunity for improving system performance or cost-efficiency. What steps did you take to implement those improvements?
• Technical: How would you design a scalable, fault-tolerant data pipeline on AWS using services like EMR, Lambda, and S3 to process tens of billions of events daily?
• Technical: Explain your experience and approach to implementing microservices using Golang and Kubernetes. What challenges have you faced and how did you overcome them?
• Technical: What strategies do you employ when optimizing SQL queries or Spark jobs in large-scale data environments to ensure performance and reliability?
• Technical: How do you leverage monitoring and alerting tools such as Datadog, Prometheus, or Grafana to maintain system health and proactively troubleshoot issues?
• Situational: Imagine a key data ingestion pipeline suddenly starts to delay data processing, impacting downstream analytics. How would you investigate and resolve this?
• Situational: If a security vulnerability were detected in the cloud infrastructure you manage, describe your process for mitigating risk while minimizing disruption to services.
• Situational: You receive conflicting requirements from business stakeholders and engineering teams about pipeline features. How do you handle prioritization and alignment?
• Company Fit: What excites you most about FreeWheel's role in the advertising technology ecosystem, and how do you see yourself contributing to that mission?
• Company Fit: How do you personally embody or relate to FreeWheel's values, such as owning the customer experience or driving results as part of a team?
• Company Fit: Why are you interested in working onsite predominantly, rather than remotely, in this position at FreeWheel?
• Questions for Interviewer: How does the Data Engineering team measure success for this role in the first 6 to 12 months?
• Questions for Interviewer: Can you describe the typical collaboration process between engineering and product teams here, especially regarding new data platform features?
• Questions for Interviewer: What are the biggest technical challenges FreeWheel anticipates in scaling data infrastructure over the next few years?