Okay, I've searched for full-time Data Engineer roles in Charlotte, NC that involve cloud lakehouse platforms such as AWS and Databricks, requiring 3+ years of experience and skills in Python, Spark, and Kafka. I have found 87 positions for you.

Sure. Here's the analysis:

Job Analysis:

This Data Engineer role within Red Ventures' Growth and Transformation team is fundamentally about architecting and evolving cloud-based data lakehouse solutions that support high-impact marketing and client growth initiatives. The candidate is expected to balance technical excellence with business impact, driving customer acquisition and retention through scalable, reliable data products and pipelines that inform key insights like lifetime value and churn propensity. Success requires deep expertise in AWS and Databricks ecosystems, proficiency in batch and streaming data processing (using tools like Kafka, Spark, Python, and Airflow), and a robust understanding of data governance and compliance frameworks (e.g., CCPA, SOC 2). This role also demands a strategic, systems-thinking mindset: crafting architectures that optimize cost and performance while ensuring data quality and observability. The Data Engineer will often serve as a bridge between technical teams (ML engineers, analysts) and business stakeholders, necessitating the ability to translate complex trade-offs into clear, actionable insights. Mentorship and leadership over junior engineers are key, indicating a mid-to-senior level position with expectations to influence best practices and technical direction. Navigating ambiguous challenges around data mesh concepts, personalization use cases, and AI integration further underlines the role’s complexity and importance within RVGT’s growth focus. Early success may be judged on the delivery of scalable, production-ready data pipelines, seamless collaboration across teams, tangible uplift in data governance, and demonstrated leadership in technical excellence and mentorship.

Company Analysis:

Red Ventures operates as a digitally native leader in consumer decision-making, encompassing a diverse set of brands that span from financial advisory to travel and beyond. This positioning as a growth-focused digital innovator means the Data Engineer role sits at the intersection of cutting-edge technology and rapidly evolving market demands, with ample opportunity for impact at scale. The company values diversity, innovation, and social responsibility—as seen in its longstanding mentorship initiatives like Road to Hire—reflecting a culture that prizes inclusive collaboration and real-world impact. Organizationally, the Data Engineer will likely function within a cross-functional team, engaging regularly with both technical peers and business leaders, suggesting a high degree of visibility and influence. The company’s modern technology stack signals a progressive environment eager for continuous improvement and agile delivery. Strategically, this hire appears designed to bolster Red Ventures’ ability to refine client growth through data-driven personalization and AI-enriched decision tools, aligning with the company’s mission to innovate consumer experiences. Long-term, this role offers growth and autonomy within a company committed to scaling data-driven initiatives and fostering employee development within a mission-oriented, collaborative culture.

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

• Tell me about a time when you had to design a data pipeline that balanced cost, performance, and governance. What trade-offs did you consider and how did you communicate these to stakeholders?
• Describe a situation where you mentored a junior engineer. How did you approach explaining complex technical concepts and ensuring their growth?
• Can you provide an example of how you have implemented data governance controls (e.g., using Monte Carlo or Collibra)? What challenges did you face and how did you address them?
• How have you handled ambiguity when designing or evolving a data architecture, especially in scenarios involving both batch and streaming data?
• Walk me through your experience building data lakehouse architectures using AWS and Databricks. What are the key design principles you always follow?
• How would you approach developing a real-time streaming pipeline using Kafka and Spark Structured Streaming that supports marketing personalization use cases?
• Explain your experience with orchestration tools like Airflow and CI/CD pipelines using GitHub Actions and Terraform. How do you ensure pipeline reliability and maintainability?
• What methods do you use for SQL performance tuning in complex warehousing environments like Snowflake or Databricks SQL?
• Imagine a scenario where your data pipelines consistently encounter data quality issues that impact downstream AI applications. How would you diagnose and resolve this?
• Suppose a business stakeholder requests a new data product to improve customer retention insights but provides limited technical detail. How would you gather requirements and ensure delivery?
• Why does working at Red Ventures appeal to you, and how do you see your skills supporting our mission to innovate the consumer journey?
• How do you align your personal values with Red Ventures’ focus on diversity, inclusion, and giving back through programs like Road to Hire?
• What excites you most about working with a modern data stack that includes tools like Databricks, Kafka, dbt, and Monte Carlo?
• How do you stay current with emerging technologies in data engineering, particularly in areas like data mesh and AI-driven analytics?
• What questions do you have about how the Data Engineering team collaborates with other groups such as ML engineers or marketing, or about Red Ventures’ data governance culture?