While the exact questions will vary based on your interviewers and the natural flow of the conversation, reviewing common patterns will help you structure your thoughts. The goal is not to memorize answers, but to prepare flexible narratives that highlight your deep expertise.

Databricks and Spark Internals

These questions test your granular understanding of the execution engine and how you optimize workloads at scale.

• How does the Catalyst Optimizer work, and how can you leverage it to improve query performance?
• Explain the differences between narrow and wide transformations, and how they impact cluster memory.
• Walk me through your strategies for optimizing Delta tables (e.g., Z-ordering, partitioning, vacuuming).
• How do you manage state in a Structured Streaming application, and what happens if the cluster restarts?
• Describe a time you had to refactor a highly inefficient PySpark job. What specific changes did you make?

System Design and Architecture

These questions evaluate your ability to design robust, secure, and cost-effective data platforms.

• Design an end-to-end real-time pipeline that ingests data from Kafka, processes it in Databricks, and serves it to a BI tool.
• How would you design a centralized governance model using Unity Catalog for an organization with hundreds of data users?
• Explain your approach to implementing CI/CD for Databricks notebooks and jobs using Azure DevOps.
• How do you ensure data quality and handle schema evolution in a production Delta Lake environment?
• What is your strategy for disaster recovery and high availability in a Databricks architecture?

Behavioral and Leadership

These questions assess your cultural alignment with Alaska Airlines and your ability to lead without formal authority.

• Tell me about a time you identified a significant cost-saving opportunity in a cloud environment and drove its implementation.
• Describe a situation where you had to mentor an engineer who was struggling with a complex technical concept.
• How do you balance the need to deliver a project quickly with the need to establish rigorous engineering standards?
• Give an example of a time you had to push back on a request from a business stakeholder because it violated architectural best practices.
• How do you embody the value of "owning safety" in the context of data engineering and platform reliability?

Leadership, Mentorship, and Collaboration

Because you will be defining the long-term strategy for Alaska Air Group, your ability to influence others is just as important as your coding skills. Interviewers want to see how you champion new features, document best practices, and resolve technical disagreements with Enterprise Architecture and Security teams.

Be ready to go over:

• Mentorship – How you upskill data scientists, analysts, and junior engineers on Spark internals and advanced debugging.
• Technical Advocacy – Evaluating new Databricks features and driving their adoption across the broader engineering organization.
• Cross-functional Collaboration – Gathering requirements from business stakeholders and translating them into robust technical specifications.
• Agile Methodologies – Estimating effort, establishing timelines, and leading development in a Scrum or Kanban environment.

Example questions or scenarios:

• "Tell me about a time you had to convince a skeptical security or architecture team to adopt a new data technology."
• "How do you approach creating documentation and standards for a diverse group of data practitioners?"
• "Describe a situation where a project was falling behind schedule due to technical debt. How did you lead the team back on track?"

Key Responsibilities

As a Principal Data Engineer, your day-to-day work revolves around advancing the Enterprise Data platform. You will spend a significant portion of your time designing and implementing high-performance batch and real-time data pipelines using Apache Spark, Delta Live Tables, and Structured Streaming. You are the go-to expert for troubleshooting complex performance bottlenecks, resolving streaming latency challenges, and optimizing job execution plans for maximum speed and cost efficiency.

Beyond writing code, you will take ownership of platform governance and reliability. This involves implementing and managing Unity Catalog for centralized data lineage and access control, as well as establishing rigorous CI/CD pipelines and automated unit testing frameworks. You will also lead efforts to standardize tagging and metadata practices across the environment to improve cost attribution and reporting.

Collaboration is a massive part of this role. You will work directly with data scientists, analysts, Enterprise Architecture, and Security teams to deliver production-grade solutions. A key responsibility is acting as a forward-thinker—constantly evaluating new Databricks capabilities, championing their usage, and mentoring other engineers through brown bag sessions, seminars, and hands-on code reviews.

Role Requirements & Qualifications

To be highly competitive for this Principal-level role at Alaska Airlines, you must bring a deep, specialized skill set alongside proven leadership capabilities. The company is looking for a seasoned professional who can operate with considerable latitude and initiative.

• Must-have technical skills – Expert-level proficiency in Databricks, Apache Spark, Python, SQL, and PySpark. You must have hands-on experience with real-time streaming (Structured Streaming, Kafka) and Infrastructure as Code (Terraform, ARM).
• Must-have experience – At least 7 years of experience in data engineering and big data platforms, with a proven track record of optimizing pipelines for performance, reliability, and cost.
• Must-have soft skills – Excellent communication skills, the ability to lead technical debates, and a passion for mentoring diverse groups of people. You must be able to collaborate effectively with cross-functional teams to gather requirements and write technical specifications.
• Nice-to-have skills – Familiarity with Azure, MLflow, and Lakehouse Federation. Experience with Agile (Scrum/Kanban) methodologies and project estimation is highly valued.
• Nice-to-have certifications – Databricks Certified Data Engineer Professional or Azure Solutions Architect Expert certifications will make your profile stand out significantly.

Frequently Asked Questions

Q: How deeply do I need to know Azure vs. Databricks? While the role touches on Azure (and CI/CD via Azure DevOps), your absolute core competency must be Databricks and Apache Spark. You should understand how Databricks integrates with Azure infrastructure, but the deepest technical grilling will be on Spark internals, Delta Lake, and streaming.

Q: What is the culture like on the corporate data teams at Alaska Airlines? The culture is highly collaborative and deeply rooted in the company's core values. There is a strong emphasis on "doing the right thing" and "being caring and kind." You are expected to be a technical powerhouse, but arrogance or a lack of willingness to mentor others will be a major red flag.

Q: How much preparation time is typical for this interview process? Given the Principal level of the role, candidates typically spend 2 to 4 weeks preparing. You should spend significant time reviewing advanced Spark optimization techniques, Unity Catalog documentation, and practicing system design communication.

Q: Does this role require being onsite? The position is located at the SeaTac, WA hub. While hybrid flexibility may exist depending on team policies, you should expect to be closely connected to the Seattle headquarters to collaborate effectively with Enterprise Architecture and operational leaders.