Okay, I've searched for Data Engineer, Analytics roles in Frankfort, KY within the Internet industry, full-time schedule, requiring a Bachelor's degree and 5+ years of experience. I have found 124 positions for you.

Sure. Here's the analysis:

Job Analysis:

The Data Engineer, Analytics role at Meta is fundamentally about building and maintaining the robust data infrastructure that empowers informed decision-making across multiple products and domains. This is not just a technical role but a strategic one, requiring the candidate to design sophisticated data models and pipelines that integrate diverse, large-scale data sources, both structured and unstructured. The responsibilities extend beyond coding and modeling: this role demands deep collaboration with cross-functional partners such as engineers, product managers, and data scientists to ensure that the data solutions are aligned with business needs and can effectively surface actionable insights through visualizations. The emphasis on SLAs, security models, data governance, and data quality highlights the critical importance of reliability and compliance in a complex environment where data privacy and protection are paramount. The ideal candidate must be highly skilled in ETL and big data ecosystem design, object-oriented programming, SQL, and Python, with the experience and judgment to navigate and optimize data flow in a high-impact setting. Solving ambiguous problems, balancing competing priorities, and influencing cross-team initiatives are core challenges. Success in the first 6 to 12 months likely includes delivering scalable and secure data warehousing solutions, improving data quality and accessibility, and demonstrating proactive collaboration that drives measurable business impact.

Company Analysis:

Lensa operates at the intersection of recruitment and cutting-edge AI-driven technology, positioning itself as a forward-thinking innovator in the talent acquisition space. The company’s product philosophy—leveraging machine learning to personalize and streamline job matching—signals a fast-paced and innovation-driven culture that highly values data science, continuous improvement, and user-centric solutions. Given its significant growth and global footprint, Lensa embraces complexity and is likely to prioritize agility, experimentation, and cross-team collaboration. The presence of a sizable R&D group in Europe alongside U.S. operations suggests a diverse, distributed environment where roles like the Data Engineer must be adept at remote communication and influencing stakeholders across time zones and disciplines. For a candidate considering this role, understanding Lensa’s mission to reshape job search through intelligent technology is key to thriving, as is being comfortable navigating a dynamic startup-like atmosphere within a high-profile, global career platform. The role will sit at a pivotal strategic point—directly enabling Lensa’s core functionality and capacity to scale—making it a high-visibility position with ample opportunity to contribute meaningfully to both product excellence and business growth.

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

• Behavioral: Tell me about a time when you had to collaborate with multiple teams to design and implement a complex data solution. How did you ensure alignment and success?
• Behavioral: Describe an instance when you identified a data quality issue that could impact decision making. How did you address it?
• Behavioral: Can you share a situation where you had to balance competing priorities in a fast-paced environment? How did you manage your time and resources?
• Behavioral: Explain a time when you influenced product or cross-functional teams based on your data insights. What approach did you take?
• Technical: How would you design a data warehouse schema to support multiple use cases while ensuring scalability and efficiency?
• Technical: Walk me through your approach to optimizing an ETL pipeline processing both structured and unstructured data sources.
• Technical: What techniques and tools do you use to implement and enforce data governance and security policies within a data platform?
• Technical: How do you manage and monitor SLAs for data availability and accuracy in your previous projects?
• Situational: Imagine a critical data pipeline has failed just before a major product release. How would you respond to resolve the issue quickly while communicating with stakeholders?
• Situational: Suppose you notice inconsistencies in data from different sources used across products. How would you approach diagnosing and fixing the problem?
• Situational: If you are asked to convince a non-technical product team about the importance of improving data quality controls, what strategy would you use?
• Company Fit: Why are you interested in working at Lensa and supporting a platform focused on transforming job search through AI?
• Company Fit: How do your personal values and working style align with a fast-paced, innovation-driven company like Lensa?
• Company Fit: What excites you most about applying your data engineering skills within a globally distributed team environment?
• Questions to Ask Interviewer: Can you describe the current data engineering team structure and how this role collaborates with product and data science stakeholders?
• Questions to Ask Interviewer: What are the biggest challenges the data engineering team at Lensa is facing right now, especially with respect to scaling and data governance?
• Questions to Ask Interviewer: How does Lensa measure the impact and success of data engineering initiatives on the overall product experience?