What is a Data Engineer at Axs?

As a Data Engineer (specifically operating as a Database Engineer II) at Axs, you are stepping into a role that sits at the very core of our live entertainment platform. Axs powers ticketing for some of the largest venues, sports teams, and artists in the world. When a highly anticipated concert goes on sale, our systems must instantly handle massive spikes in traffic, concurrent transactions, and complex data routing without missing a beat. Your work ensures that the underlying data infrastructure is resilient, scalable, and highly performant under this immense pressure.

Your impact in this position extends directly to the fan experience and our business operations. By designing robust data models, optimizing database performance, and building reliable data pipelines, you enable seamless ticket purchasing and provide our analytics teams with the real-time insights they need. You are not just moving data; you are ensuring that millions of fans secure their tickets fairly and efficiently, while protecting the system from malicious bot activity.

This role requires a unique blend of traditional database administration and modern data engineering. You will be expected to tackle complex concurrency challenges, handle vast amounts of transactional data, and collaborate closely with backend engineering teams. If you thrive in high-stakes environments where your technical decisions directly impact the success of global events, this role at Axs will be both deeply challenging and incredibly rewarding.

Common Interview Questions

Getting Ready for Your Interviews

Preparing for an interview at Axs requires a strategic approach to both your technical fundamentals and your understanding of high-scale systems. You should treat this preparation as an opportunity to showcase how you think under pressure.

Technical Proficiency – At Axs, this means demonstrating a deep understanding of relational and NoSQL databases, query optimization, and data pipeline architecture. Interviewers will evaluate your ability to write clean, efficient code (typically SQL and Python) and your knowledge of database internals. You can demonstrate strength here by explaining the "why" behind your technical choices, such as why a specific index improves performance or why a certain data model fits a transactional workload.

System Design and Scalability – Because our ticketing platform experiences extreme traffic spikes, we need engineers who understand high availability and distributed systems. Interviewers will look at how you structure data architectures to handle millions of concurrent reads and writes. You can excel in this area by proactively discussing trade-offs, bottlenecks, and scaling strategies in your design solutions.

Problem-Solving Ability – We evaluate how you approach ambiguous, complex challenges. At Axs, you will rarely be handed a perfectly defined problem. Interviewers want to see you break down a vague prompt, ask clarifying questions, and iteratively build a solution. Strong candidates think out loud and pivot gracefully when presented with new constraints.

Cross-Functional Collaboration – Data Engineers at Axs do not work in silos; you will partner with product managers, software engineers, and data scientists. We assess your communication skills and your ability to translate complex technical concepts into business value. You should be prepared to share examples of how you have influenced team decisions, managed stakeholder expectations, and navigated disagreements.

Interview Process Overview

The interview process for a Database Engineer II at Axs is designed to be rigorous but collaborative. We want to see how you operate in real-world scenarios, so our evaluations heavily index on the types of challenges you will actually face on the job. The process generally begins with a recruiter phone screen to align on your background, compensation expectations, and location preferences (such as our Frisco, TX office).

If there is a mutual fit, you will move on to a technical phone screen. This typically involves a mix of conceptual database questions and live coding, focusing heavily on SQL and data manipulation. We are looking for fluency and efficiency here. Following a successful technical screen, you will be invited to a virtual onsite loop. This onsite consists of several rounds, including a deep dive into database architecture and system design, a behavioral and past-experience interview, and further technical evaluations covering data modeling and performance tuning.

Throughout this process, our philosophy is to evaluate your practical engineering skills rather than your ability to memorize trivia. We care about how you collaborate, how you handle feedback during a live technical discussion, and how you approach problems at the scale of live entertainment ticketing.

The visual timeline above outlines the typical progression of your interview stages, from the initial recruiter screen through the final virtual onsite rounds. You should use this timeline to pace your preparation, focusing first on core SQL and coding fundamentals before shifting your energy to complex system design and behavioral storytelling for the onsite stages. Keep in mind that the exact order of onsite modules may vary depending on interviewer availability, but the core competencies evaluated will remain consistent.

Deep Dive into Evaluation Areas

To succeed in the Axs interview process, you must demonstrate mastery across several key technical and behavioral domains. Our interviewers use targeted questions and scenarios to assess your depth of knowledge and practical experience.

Database Architecture and Data Modeling

• This area is critical because the way data is structured directly dictates the performance of our ticketing platform. We evaluate your ability to design schemas that support high-volume transactional workloads (OLTP) as well as analytical queries (OLAP). Strong performance means you can confidently normalize data to ensure integrity, while knowing exactly when to denormalize for read performance.
• Relational vs. NoSQL – Understanding when to use a relational database (like PostgreSQL) versus a NoSQL solution (like DynamoDB or MongoDB) based on transaction requirements and scalability needs.
• Schema Design – Crafting entity-relationship diagrams, defining primary/foreign keys, and managing constraints.
• Data Warehousing Concepts – Familiarity with star and snowflake schemas, and designing models for downstream analytics.
• Advanced concepts (less common) – Multi-tenant database design, handling schema migrations with zero downtime, and event-driven data modeling.
• "Design a database schema to handle a high-demand concert on-sale, ensuring that no two users can purchase the same seat simultaneously."
• "Walk me through how you would model a fan's purchase history to support both fast application load times and complex marketing analytics."
• "Explain the trade-offs between a highly normalized schema and a denormalized schema in a heavily read-heavy environment."

Query Optimization and Performance Tuning

• At Axs, a slow query during a major ticket drop can cascade into system-wide failures. We evaluate your ability to identify bottlenecks, read execution plans, and optimize database performance. A strong candidate doesn't just write SQL that works; they write SQL that scales.
• Indexing Strategies – Deep knowledge of B-tree indexes, hash indexes, composite indexes, and understanding how the query optimizer utilizes them.
• Execution Plans – Analyzing EXPLAIN plans to identify sequential scans, costly joins, and areas for optimization.
• Concurrency Control – Managing locks, isolation levels, and deadlocks in a high-transaction environment.
• Advanced concepts (less common) – Query rewriting techniques, materialized views for performance, and tuning database configuration parameters (e.g., memory allocation, vacuuming).
• "Given this slow-performing query and its execution plan, how would you optimize it to run in under 100 milliseconds?"
• "Explain how you would troubleshoot and resolve a sudden spike in database deadlocks during a peak traffic event."
• "Describe a time you had to optimize a critical database query. What was the impact, and what specific steps did you take?"

ETL and Data Pipeline Engineering

• While this role leans heavily into database engineering, you must also be capable of moving data efficiently across systems. We evaluate your ability to build robust, fault-tolerant pipelines that extract, transform, and load data reliably. Strong performance involves discussing error handling, data quality checks, and orchestration.
• Batch vs. Streaming – Understanding the differences between processing data in scheduled batches versus real-time streaming (e.g., using Kafka).
• Orchestration Tools – Experience with tools like Airflow or step functions to manage complex pipeline dependencies.
• Data Quality – Implementing monitoring and alerting to catch missing, delayed, or corrupt data before it impacts stakeholders.
• Advanced concepts (less common) – Change Data Capture (CDC) architectures, idempotent pipeline design, and handling late-arriving data.
• "Design an ETL pipeline that extracts daily transaction data from our primary databases, transforms it for analytics, and loads it into a data warehouse."
• "How do you ensure data integrity and handle failures in a pipeline that processes millions of events per hour?"
• "Tell me about a time a critical data pipeline failed in production. How did you diagnose the issue, and how did you prevent it from happening again?"

Behavioral and Cross-Functional Leadership

• Technical skills alone are not enough; you must be able to operate effectively within the Axs culture. We evaluate your communication, your ownership of projects, and how you handle adversity. A strong candidate provides structured, concise answers (using the STAR method) that highlight their specific contributions and learnings.
• Stakeholder Management – Communicating technical constraints to non-technical product managers or business leaders.
• Navigating Ambiguity – Taking a vague requirement and turning it into a concrete engineering plan.
• Ownership and Impact – Demonstrating a track record of seeing projects through from conception to deployment and measuring their success.
• "Tell me about a time you disagreed with a senior engineer or architect on a technical design. How did you resolve it?"
• "Describe a situation where you had to deliver a critical project with incomplete requirements."
• "Share an example of a time you identified a process or system bottleneck and took the initiative to fix it without being asked."