Applied Systems Data Engineer interview questions & guide 2026

Preparing for the Data Engineer interview at Applied Systems requires a balanced focus on cloud architecture, deep SQL expertise, and pipeline automation. We evaluate candidates holistically, looking for both technical depth and the ability to collaborate effectively.

Focus your preparation on the following key evaluation criteria:

• Cloud-Native Data Architecture – We assess your hands-on experience with GCP services (BigQuery, Pub/Sub, Dataflow) and your ability to design scalable data lake architectures. Strong candidates can articulate the trade-offs between different storage and processing solutions.
• Pipeline Engineering & Automation – You will be evaluated on your ability to build robust ETL/ELT pipelines and automate their deployment. Demonstrating proficiency with CI/CD tools, Terraform, and Kubernetes will set you apart.
• Data Modeling & Query Optimization – Interviewers will look closely at your ability to design efficient schemas (star/snowflake) and write complex, highly optimized SQL. You must show how you diagnose and resolve performance bottlenecks.
• BI Integration & Tooling – Because Looker is central to our data-driven culture, your understanding of BI performance metrics, LookML codebases, and derived tables is a critical evaluation point.
• Problem-Solving & Agility – We value candidates who thrive in fast-paced environments, can navigate ambiguity, and champion continuous improvement in internal processes and documentation.

The interview process for the Data Engineer role is designed to assess your technical capabilities while giving you a clear window into our engineering culture. You will begin with an initial recruiter phone screen, which focuses on your background, your familiarity with the GCP ecosystem, and your alignment with our core values. This is a conversational step meant to ensure mutual fit regarding location (remote or in-office) and basic technical prerequisites.

Following the initial screen, you will move into the technical evaluation phases. This typically involves a technical screening with a senior engineer or hiring manager, focusing on your experience with BigQuery, ETL pipelines, and data modeling. If successful, you will advance to a comprehensive virtual onsite loop. This loop consists of multiple sessions covering deep-dive SQL and Python coding, system design tailored to cloud data architectures, and behavioral interviews to assess how you collaborate cross-functionally within an agile framework.

Our process emphasizes real-world problem-solving over abstract puzzles. We want to see how you approach the types of challenges you will actually face on the EZLynx data team, such as troubleshooting performance issues in a data lake or optimizing a slow-running query backing a critical Looker dashboard.

This visual timeline outlines the typical progression from the initial recruiter screen through the final technical and behavioral rounds. Use it to pace your preparation, ensuring you are ready for high-level architectural discussions as well as hands-on coding and query optimization tasks. Keep in mind that the exact order of the virtual onsite rounds may vary based on interviewer availability.

Cloud Data Engineering & GCP Expertise

Because our infrastructure relies heavily on Google Cloud Platform, your mastery of GCP services is heavily scrutinized. Interviewers want to see that you can not just use these tools, but that you understand how to string them together to build resilient, scalable data workflows. Strong performance here means demonstrating a deep understanding of IAM, cloud resource monitoring, and event-driven architectures.

Be ready to go over:

• BigQuery Architecture – Partitioning, clustering, slot utilization, and cost optimization strategies.
• Streaming vs. Batch Processing – Knowing when to use Pub/Sub and Dataflow versus traditional batch ETL jobs.
• Cloud Automation – Managing infrastructure as code using Terraform and deploying services via Kubernetes and Helm.
• Advanced concepts (less common) – Integrating custom Cloud Functions for lightweight data transformations or event triggers.

Example questions or scenarios:

• "Walk us through a time you designed a scalable data pipeline using Pub/Sub and Dataflow. How did you handle late-arriving data?"
• "How do you manage IAM roles and permissions securely when setting up a new data lake environment in GCP?"

Data Modeling & Advanced SQL

Data modeling and SQL are the bedrock of this role. You will be tested on your ability to translate complex business reporting requirements into efficient, logical data models. Interviewers expect you to write complex queries seamlessly and, more importantly, to know how to optimize them when they perform poorly.

Be ready to go over:

• Schema Design – Designing star and snowflake schemas, and understanding the nuances of normalization versus denormalization in a columnar database like BigQuery.
• Advanced SQL Functions – Utilizing window functions, CTEs (Common Table Expressions), and stored procedures effectively.
• Query Optimization – Reading execution plans, identifying bottlenecks, and rewriting queries for maximum efficiency.
• Advanced concepts (less common) – Implementing Change Data Capture (CDC) using tools like Debezium or Kafka to feed your data models in real-time.

Example questions or scenarios:

• "Given a highly denormalized dataset in BigQuery, how would you optimize a query that is currently timing out due to heavy window function usage?"
• "Explain your approach to designing a star schema for a new insurance claims reporting dashboard. What fact and dimension tables would you create?"

BI Performance & Looker Integration

A unique aspect of this role is the heavy emphasis on Looker. You are not just delivering data to a warehouse; you are responsible for the performance of the dashboards that business users rely on. Interviewers will look for your ability to bridge the gap between backend data engineering and frontend BI performance.

Be ready to go over:

• LookML & Derived Tables – Writing efficient LookML and knowing when to use persistent derived tables (PDTs) versus standard views.
• Dashboard Performance – Diagnosing slow-loading Explores and optimizing the underlying queries backing Looker datasets.
• Monitoring & Tooling – Developing custom dashboards and tools to monitor Looker performance metrics.

Example questions or scenarios:

• "A critical Looker dashboard is taking over two minutes to load. Walk me through your step-by-step process for diagnosing and fixing the performance issue."
• "When would you choose to build a persistent derived table in Looker versus handling the transformation directly in your BigQuery ETL pipeline?"

Pipeline Automation & CI/CD

We operate with high velocity, which means manual deployments are a non-starter. You will be evaluated on your software engineering best practices applied to data workflows. Strong candidates treat data infrastructure as code and champion principles-based approaches to testing and deployment.

Be ready to go over:

• CI/CD Pipelines – Building and maintaining automated workflows using GitHub Actions or Jenkins.
• Version Control – Managing LookML and SQL codebases effectively using Git.
• Scripting & Orchestration – Using Python or Bash (and potentially Go) to support custom data processing tools and orchestrate workflows.

Example questions or scenarios:

• "Describe how you would set up a CI/CD pipeline for a new set of BigQuery stored procedures and LookML updates."
• "Tell us about a time you used Python to automate a manual data quality check within your ETL pipeline."

As a Data Engineer at Applied Systems, your day-to-day work revolves around building and maintaining the connective tissue between our raw data sources and our business intelligence platforms. You will spend a significant portion of your time designing and deploying scalable ETL/ELT pipelines using GCP native services like BigQuery, Pub/Sub, and Dataflow. This involves not only writing the code but also automating its deployment using Terraform, Kubernetes, and Helm, ensuring our infrastructure remains robust and reproducible.

Collaboration is a massive part of this role. You will work closely with data analysts, software engineers, and product managers across the EZLynx team to understand reporting needs and translate them into efficient, logical data models. When business users report slow dashboards, you will dive into Looker and BigQuery to diagnose and address performance issues in LookML codebases, Explores, and derived tables, ensuring our datasets are backed by highly performant queries.

For those stepping into the Senior Data Engineer level, your responsibilities will expand to include significant architectural oversight. You will assess the risks and opportunities of various technical solutions, guiding the team's decisions on scalability and security. You will also champion continuous improvement, refining our internal processes, optimizing complex data lake operations, and mentoring team members as we evolve our data infrastructure over time.

To thrive as a Data Engineer at Applied Systems, you need a strong blend of cloud infrastructure knowledge, data modeling expertise, and software engineering discipline.

• Must-have technical skills – You must have 3+ years of hands-on experience with Google BigQuery for data warehousing and a solid grasp of the broader GCP ecosystem (Pub/Sub, Dataflow, Cloud Functions, IAM). Proficiency in writing and optimizing complex SQL (including window functions and stored procedures) is non-negotiable. You also need strong scripting skills in Python or Bash and experience building ETL pipelines for large datasets.
• Must-have operational skills – Experience with CI/CD pipelines (GitHub Actions, Jenkins), version control (Git), and data modeling best practices (star/snowflake schemas) is required. You must also have experience managing Looker environments and diagnosing BI performance issues.
• Nice-to-have skills – Familiarity with Change Data Capture (CDC) tools like Debezium or Kafka, experience with Go programming, and hands-on proficiency with Kubernetes and Terraform will make your application stand out significantly.
• Senior-level expectations – If applying for the Senior role, you must bring 5+ years of BigQuery experience, advanced Python and SQL optimization skills, and a proven track record of making architectural decisions that impact data lake operations and security.
• Soft skills – The ability to work cross-functionally in an agile, fast-paced environment is essential. You must be a strong communicator who can translate technical constraints to non-technical stakeholders and champion best practices across the team.

Q: How difficult is the technical interview process? The technical rounds are rigorous but practical. We do not focus on obscure algorithmic brainteasers; instead, we test your ability to write clean Python, optimize complex SQL, and design realistic cloud architectures. Expect to spend a solid week or two brushing up on BigQuery optimization, LookML concepts, and GCP services.

Q: What is the difference between the mid-level and senior-level interviews? While both levels test core engineering skills, Senior candidates will face much deeper system design questions. Senior interviews focus heavily on your ability to evaluate trade-offs, design secure data lake architectures, and demonstrate how you have mentored teams and improved internal engineering processes.

Q: Does this role require being in an office? No, this position offers significant flexibility. You can choose to work fully remotely within the United States or from an Applied Systems office. We flex our time together and collaborate effectively across distributed teams.

Q: What is the culture like on the EZLynx data team? The culture is highly collaborative, agile, and values-driven. We believe that you are a teammate, not just an employee. You will find a fast-paced environment where trying new things is encouraged, and where cross-functional collaboration with analysts and software engineers is a daily reality.

Q: How long does the entire interview process usually take? Typically, the process takes about 3 to 4 weeks from the initial recruiter screen to an offer decision, depending on scheduling availability for the virtual onsite loop.

• Think in "Cloud Native" Terms: When answering architecture questions, default to GCP-native solutions (like Pub/Sub, Dataflow, and BigQuery) as outlined in the job description. Show that you understand the Google Cloud ecosystem deeply, rather than just generic data engineering concepts.

• Brush Up on Looker: Many Data Engineers focus solely on the backend. Because managing the Looker instance is a core responsibility here, reviewing LookML best practices, PDT management, and dashboard performance tuning will give you a massive advantage over other candidates.
• Showcase Your DevOps Mindset: Applied Systems values automation. Whenever discussing pipelines, proactively mention how you would use Terraform for infrastructure, Kubernetes for deployment, and GitHub Actions for CI/CD. Treat data like software.
• Emphasize Business Impact: We build software for the insurtech industry. When discussing past projects, frame your technical achievements in terms of business outcomes—how your pipeline reduced reporting latency, saved cloud costs, or enabled better decision-making for stakeholders.

Joining Applied Systems as a Data Engineer is a unique opportunity to shape the data landscape of a leading insurtech company. You will tackle complex challenges involving massive datasets, cloud-native architectures, and critical BI integrations. By bringing your expertise in GCP, BigQuery, and pipeline automation to the EZLynx team, you will directly empower the organization to make faster, more reliable data-driven decisions.

This salary module provides the targeted starting base range for this position in the United States. Keep in mind that your actual offer will consider your depth of experience, specific skill set, and whether you are stepping into the mid-level or senior-level role, with additional eligibility for bonus plans.

To succeed in your interviews, focus your preparation on the intersection of data architecture and software engineering. Practice writing and optimizing complex SQL, review your GCP service architectures, and be ready to discuss how you automate and monitor data pipelines from end to end. Remember that we are looking for collaborative problem-solvers who are excited to learn and grow within an agile team.

You have the skills and the background to make an impact here. Review the concepts outlined in this guide, leverage additional resources on Dataford to refine your technical communication, and approach your interviews with confidence. We look forward to learning more about you and exploring how you can help us create amazing career moments at Applied Systems.