Okay, I've searched for Senior Data Engineer positions in New York City that emphasize collaboration and involve working with Snowflake and dbt, with a salary expectation of at least $180,000. I found 157 positions matching your criteria.

Sure. Here's the analysis:

Job Analysis:

The Senior Data Engineer at Horizon Media is fundamentally tasked with the pivotal role of developing and maintaining robust data pipelines, which directly influence the organization's analytics and decision-making processes. The core responsibilities include building these end-to-end pipelines using a modern tech stack that features tools like Snowflake, dbt, and Python, which indicates a strong emphasis on a hands-on technical aptitude. The requirement for collaboration with analysts and data scientists suggests that this role isn't just about technical implementation but also about understanding and translating cross-functional data needs into actionable insights. Candidates can expect to face challenges related to data quality, pipeline failures, and performance optimizations, meaning success in this role will involve a mix of technical proficiency, proactive problem-solving, and effective communication. Performance expectations encompass the reliability and speed of data systems, with early success potentially measured by the ability to streamline data processes and enhance the data reliability metrics within the first 6-12 months.

Company Analysis:

Horizon Media operates at the forefront of the marketing and advertising industry, recognized for its innovative and data-driven approach. As the third-largest U.S. media agency, its market position allows this role to have a significant impact on high-stakes organizational decisions and client outcomes. The company is renowned for its inclusive culture, which is not merely a benefit but a core component of its operational philosophy, making diversity, equity, and inclusion central to employee engagement and creativity. This cultural backdrop may set specific expectations around collaboration and innovation that a Senior Data Engineer must embody. Within the organizational structure, this role reports to the VP of Architecture & Data Engineering, suggesting a potential pathway for strategic visibility and influence. The strategic alignment here hints at investing in data capabilities as a method to fortify client outcomes, signifying this role's contribution to the broader company objectives of sustained growth and client satisfaction.

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

• Tell me about a time when you had to troubleshoot a data pipeline failure. What steps did you take to resolve the issue?
• Describe a project in which you successfully implemented complex SQL queries. What challenges did you face, and how did you overcome them?
• Can you provide an example of how you collaborated with cross-functional teams to understand and meet their data needs?
• How do you ensure data quality and integrity when building data pipelines?
• What approach would you take to optimize a slow-running SQL query? Can you walk us through your thought process?
• Imagine you are tasked with integrating a new data source into an existing pipeline using Fivetran. What steps would you follow?
• You notice a discrepancy in the data provided to a marketing analyst. How would you address this situation?
• Can you explain your experience working with AWS services in the context of data engineering?
• Why are you interested in working at Horizon Media specifically, and how do you see yourself contributing to our mission?
• Considering Horizon's strong emphasis on diversity and inclusion, how do you believe this will enhance your role as a Senior Data Engineer?
• What do you think are the key metrics for measuring the success of a data engineering team in a media agency?
• What questions do you have about the team dynamics within the data engineering department here at Horizon Media?
• How do you stay current with trends in data engineering, and are there any specific areas where you would like to deepen your knowledge?
• What challenges do you foresee in integrating data from diverse marketing platforms, and how would you approach them?
• How could we improve our internal documentation processes for data pipelines and workflows?