1. What is a Data Engineer at Bestow?

As a Data Engineer at Bestow, you are at the heart of our mission to make life insurance accessible, fast, and entirely digital. Unlike traditional insurance companies that rely on weeks of manual underwriting and medical exams, Bestow uses data to make instant, algorithmic decisions. This means the data infrastructure you build and maintain directly powers our core product, enabling real-time policy approvals and driving critical business intelligence.

In this role, you will tackle high-impact challenges related to scale, data complexity, and strategic influence. You will design, build, and optimize the pipelines that feed our underwriting algorithms, customer analytics, and operational dashboards. Whether you are interviewing for a Senior Data Engineer or a Staff Data Engineer position, your work will directly influence how our engineering, product, and data science teams operate. You will be expected to handle massive datasets with precision, ensuring data quality, security, and high availability.

Expect a fast-paced, collaborative environment where your technical decisions carry significant weight. You will not just be writing code; you will be solving complex architectural problems, mentoring peers, and driving the evolution of our modern data stack. This role requires a blend of deep technical expertise and a strong product mindset to ensure our data ecosystem scales seamlessly with our growing user base.

2. Getting Ready for Your Interviews

Preparing for an interview at Bestow requires a strategic approach. We are looking for engineers who can execute flawlessly while understanding the broader business context. Focus your preparation on demonstrating how you translate complex data challenges into reliable, scalable solutions.

Technical Proficiency – You must demonstrate deep expertise in modern data engineering tools. We evaluate your ability to write highly optimized SQL, build robust Python applications, and design efficient data pipelines using cloud-native technologies and orchestration tools. Strong candidates show they can not only write code but also debug and optimize it for production environments.

System Design and Architecture – Especially for Senior Data Engineer and Staff Data Engineer roles, we assess your ability to design scalable, fault-tolerant data architectures. You should be prepared to discuss data modeling, batch versus streaming processing, and how to build systems that ensure data integrity and high availability.

Problem-Solving and Ambiguity – Startups move fast, and requirements can be fluid. Interviewers will evaluate how you approach ambiguous, open-ended problems. We look for candidates who ask clarifying questions, identify edge cases, and propose iterative solutions rather than jumping straight to complex over-engineering.

Collaboration and Leadership – As a senior technical contributor, your ability to influence others is critical. We evaluate how you communicate technical trade-offs to non-technical stakeholders, mentor junior engineers, and drive cross-functional initiatives with product managers and data scientists.

3. Interview Process Overview

The interview process for a Data Engineer at Bestow is designed to be rigorous, collaborative, and reflective of the actual work you will do. You will begin with an initial recruiter screen to discuss your background, alignment with the role, and general compensation expectations. If there is a mutual fit, you will move on to a technical screen, which typically involves live coding focused on Python and SQL, as well as a high-level discussion of your past data projects.

Candidates who pass the technical screen will be invited to the virtual onsite loop. This stage is comprehensive and usually consists of four to five distinct rounds. You will face deep-dive technical interviews covering data modeling, pipeline architecture, and advanced coding. There will also be a dedicated behavioral and leadership round to assess your cultural alignment and ability to navigate complex team dynamics. For Staff Data Engineer candidates, expect an intensified focus on system design and cross-team technical leadership.

Throughout the process, Bestow emphasizes a collaborative interviewing philosophy. We want to see how you think, how you incorporate feedback, and how you partner with others to solve problems. The process is challenging, but it is structured to give you multiple opportunities to showcase your unique strengths.

The visual timeline above outlines the typical progression of our interview stages, from the initial screen to the final onsite rounds. Use this to pace your preparation, ensuring you allocate time for both hands-on coding practice and high-level architectural review. Keep in mind that specific rounds may vary slightly depending on whether you are interviewing for a Senior or Staff level position.

4. Deep Dive into Evaluation Areas

To succeed in your interviews, you need to understand exactly what our engineering team is looking for. Our evaluation is broken down into several core competencies that reflect the daily realities of a Data Engineer at Bestow.

Data Modeling and Architecture

Data modeling is the foundation of our analytics and underwriting systems. We evaluate your ability to design scalable schemas that balance read-and-write performance while maintaining strict data integrity. Strong performance in this area means you can confidently translate complex business requirements into logical and physical data models.

Be ready to go over:

• Dimensional Modeling – Understanding star schemas, snowflake schemas, and when to use fact versus dimension tables.
• Modern Data Stack – Experience with cloud data warehouses (like Snowflake or BigQuery) and transformation tools (like dbt).
• Data Governance – Designing systems that handle PII securely, which is critical in the insurtech space.
• Advanced concepts (less common) – Change Data Capture (CDC) patterns, slowly changing dimensions (SCDs), and data mesh architectures.

Example questions or scenarios:

• "Design a data model to track user progression through our online life insurance application funnel."
• "How would you handle late-arriving data in a daily batch pipeline?"
• "Explain how you would implement a Type 2 Slowly Changing Dimension for customer policy statuses."

Pipeline Engineering and Orchestration

Building resilient data pipelines is a core responsibility. Interviewers will test your ability to extract, transform, and load data from various sources into our central warehouse. We look for candidates who anticipate failures, build in robust logging, and understand orchestration mechanisms.

Be ready to go over:

• Batch vs. Streaming – Knowing when to use daily batch jobs versus real-time streaming for underwriting events.
• Orchestration – Designing DAGs (Directed Acyclic Graphs) using tools like Apache Airflow to manage dependencies.
• Idempotency – Ensuring pipelines can be rerun safely without creating duplicate records or corrupted states.
• Advanced concepts (less common) – Custom Airflow operators, optimizing Spark jobs, and handling API rate limits in ingestion frameworks.

Example questions or scenarios:

• "Walk me through how you would design an idempotent pipeline that ingests third-party medical data via a REST API."
• "Your Airflow DAG failed silently overnight. How do you troubleshoot and architect a solution to prevent this?"
• "Compare the trade-offs between an ETL and an ELT approach for our specific use case."

Python and SQL Proficiency

Your hands-on coding skills are evaluated through practical, real-world scenarios. We do not focus on obscure brainteasers; instead, we test your ability to manipulate data efficiently. A strong candidate writes clean, modular Python code and highly optimized SQL queries that scale across billions of rows.

Be ready to go over:

• Advanced SQL – Mastery of window functions, CTEs (Common Table Expressions), and query execution plans.
• Python Data Manipulation – Using Pandas, PySpark, or native Python data structures to clean and transform datasets.
• Performance Tuning – Identifying bottlenecks in slow-running queries and refactoring them for optimal performance.
• Advanced concepts (less common) – Writing custom UDFs (User Defined Functions) and handling complex JSON arrays in SQL.

Example questions or scenarios:

• "Write a SQL query using window functions to find the top three highest-converting user acquisition channels over a rolling 30-day period."
• "Given a messy JSON payload of user application data, write a Python script to flatten, clean, and validate the records."
• "How would you optimize a query that is performing a massive cross-join and timing out?"

Leadership and Behavioral

At the Senior Data Engineer and Staff Data Engineer levels, technical skills alone are not enough. We evaluate your ability to drive projects, influence stakeholders, and elevate the engineering culture. Strong candidates provide structured, metrics-driven examples of their past impact using frameworks like STAR (Situation, Task, Action, Result).

Be ready to go over:

• Technical Debt – Identifying, prioritizing, and resolving legacy infrastructure issues while continuing to deliver feature work.
• Cross-Functional Collaboration – Partnering with Data Science to deploy underwriting models and with Product to define tracking metrics.
• Mentorship – Guiding junior engineers through code reviews, pairing sessions, and architectural design documents.
• Advanced concepts (less common) – Driving organizational shifts toward new technologies or methodologies.

Example questions or scenarios:

• "Tell me about a time you had to push back on a product requirement because it compromised data integrity."
• "Describe a situation where you led a major migration or infrastructure overhaul. How did you manage the transition?"
• "How do you balance the need to deliver quickly with the need to build scalable, maintainable data pipelines?"

5. Key Responsibilities

As a Data Engineer at Bestow, your day-to-day work directly enables our business to scale and innovate. You will be responsible for designing, building, and maintaining the end-to-end data pipelines that ingest raw data from internal microservices and external third-party vendors. This data is critical for our automated underwriting engine, meaning your pipelines must be highly reliable, secure, and performant.

You will collaborate closely with adjacent teams. You will work alongside Software Engineers to ensure application databases are optimized for analytical extraction. You will partner with Data Scientists to operationalize machine learning models, ensuring they have clean, structured data for training and inference. Additionally, you will support Product Managers and Business Intelligence teams by building the foundational data models that power executive dashboards and operational reporting.

Beyond coding, a significant portion of your role, especially at the Senior Data Engineer and Staff Data Engineer levels, involves technical leadership. You will lead architectural design reviews, establish data governance standards, and mentor junior team members. You will frequently champion initiatives to modernize our stack, reduce pipeline latency, and automate data quality checks to ensure our business always operates on accurate information.

6. Role Requirements & Qualifications

To thrive as a Data Engineer at Bestow, you need a solid foundation in modern data architecture and a proven track record of delivering scalable solutions. We look for candidates who combine deep technical expertise with a strong sense of ownership.

• Must-have technical skills – Advanced proficiency in SQL and Python. Deep experience with cloud data warehouses (e.g., Snowflake, BigQuery, Redshift) and orchestration tools (e.g., Apache Airflow, Dagster). Strong understanding of data modeling principles and ELT/ETL architectures.
• Must-have experience – Typically 5+ years of dedicated data engineering experience for Senior roles, and 8+ years for Staff roles. Proven experience building and maintaining production-grade data pipelines at scale.
• Nice-to-have skills – Experience with streaming technologies (e.g., Kafka, Kinesis), data transformation frameworks like dbt, and Infrastructure as Code (e.g., Terraform). Familiarity with the insurtech or fintech domains is highly valued.
• Soft skills – Exceptional communication skills, the ability to translate business requirements into technical specifications, and a demonstrated history of mentoring peers and leading cross-functional projects.

7. Common Interview Questions

The questions below represent the types of challenges you will face during your interviews at Bestow. While you should not memorize answers, use these to understand the patterns and depth of knowledge we expect from strong candidates.

SQL and Data Transformation

We test your ability to write performant queries and transform raw data into usable formats for analytics and reporting.

• Write a SQL query to identify duplicate policy applications within a 24-hour window for the same user.
• How do you handle missing or malformed data when aggregating monthly revenue metrics?
• Explain the difference between a RANK(), DENSE_RANK(), and ROW_NUMBER() function, and provide a use case for each.
• Write a query to pivot a table of customer interaction events into a wide format for a machine learning model.
• How would you optimize a query that scans a billion-row fact table but only needs to aggregate data for the last 7 days?

Python and Algorithmic Thinking

We evaluate your ability to write clean, modular Python code to manipulate data structures and interact with APIs.

• Write a Python function to parse a complex, nested JSON response from a third-party medical API and extract specific risk factors.
• How would you implement a retry mechanism with exponential backoff for an API integration that frequently times out?
• Given a large CSV file that cannot fit into memory, write a Python script to process it in chunks and calculate aggregate statistics.
• Describe how you would write unit tests for a Python-based data transformation module.
• Implement a function to merge two overlapping time-series datasets, prioritizing the most recent data points.

System Design and Architecture

For Senior and Staff roles, we assess your ability to design end-to-end data systems that are scalable, secure, and maintainable.

• Design a data architecture to ingest, process, and store real-time clickstream data from our application funnel.
• Walk me through how you would migrate our core data warehouse from Redshift to Snowflake with zero downtime.
• How would you design a data quality monitoring system to alert the team if a critical pipeline produces anomalous data?
• Design an ELT pipeline using dbt and Airflow to process daily snapshots of our production database.
• How do you ensure compliance and secure handling of PII data throughout your proposed data architecture?

Behavioral and Leadership

We look for candidates who demonstrate ownership, navigate ambiguity, and foster a collaborative team environment.

• Tell me about a time a critical data pipeline broke in production. How did you handle the immediate crisis, and what did you do to prevent it from happening again?
• Describe a situation where you disagreed with a Data Scientist or Product Manager about a technical implementation. How did you resolve it?
• Walk me through a complex data project you led from inception to delivery. What were the biggest roadblocks?
• How do you approach mentoring junior engineers who are struggling with complex data modeling concepts?
• Tell me about a time you had to balance building a "perfect" technical solution with meeting a tight business deadline.

8. Frequently Asked Questions

Q: How difficult is the technical screen, and how should I prepare? The technical screen focuses heavily on practical Python and SQL skills. It is less about tricky algorithms and more about writing clean, efficient code to solve realistic data manipulation problems. Review advanced SQL concepts and practice parsing and transforming JSON/CSV data in Python.

Q: What is the main difference in expectations between the Senior and Staff Data Engineer roles? While both roles require deep technical execution, Staff Data Engineer candidates are evaluated heavily on their ability to drive architecture across multiple teams, influence technical strategy, and solve highly ambiguous, organization-wide data challenges. Senior candidates focus more on executing complex projects within their immediate team.

Q: What is the engineering culture like at Bestow? Bestow operates with a strong startup mentality—we value agility, ownership, and cross-functional collaboration. Data is the lifeblood of our product, so the Data Engineering team is highly respected and deeply integrated with Product and Data Science. Expect a culture that prioritizes impact over rigid processes.

Q: Does Bestow support remote work for these roles? While the job postings highlight Dallas, TX, Bestow generally supports a flexible hybrid or remote working model depending on the specific team's needs. Be sure to clarify your location preferences and the team's working cadence with your recruiter during the initial screen.

Q: How long does the interview process typically take? The end-to-end process usually takes between 3 to 5 weeks, depending on scheduling availability. We strive to move quickly and provide prompt feedback after the virtual onsite loop.

9. Other General Tips

• Think Out Loud: During technical rounds, your thought process is just as important as the final solution. Communicate your assumptions, explain your trade-offs, and talk through your logic before you start writing code.
• Clarify Ambiguity: Interviewers often provide open-ended prompts intentionally. Before designing a system or writing a query, ask questions about data volume, latency requirements, and the end-user of the data.

• Focus on Business Value: Always tie your technical decisions back to the business impact. When discussing past projects, highlight how your pipelines improved efficiency, reduced costs, or enabled new product features.
• Understand the Insurtech Context: Familiarize yourself with the challenges of handling sensitive data (PII/HIPAA). Demonstrating an understanding of data governance and security will significantly strengthen your candidacy.
• Be Ready to Discuss Trade-offs: There is rarely a perfect technical solution. Be prepared to discuss the pros and cons of different tools, architectural patterns, and data models. Acknowledging the limitations of your own designs shows maturity and deep understanding.

10. Summary & Next Steps

Interviewing for a Data Engineer position at Bestow is an opportunity to showcase your ability to build the critical infrastructure that powers a modern, digital-first life insurance platform. The role offers the chance to work with massive datasets, solve complex architectural challenges, and directly influence the company's strategic direction. By focusing your preparation on practical coding, scalable system design, and impactful behavioral examples, you will position yourself as a standout candidate.

Remember that our interviewers are looking for a collaborative partner—someone who can execute technically while navigating the fast-paced, ambiguous environment of a growing company. Approach each round with confidence, communicate your thought process clearly, and do not hesitate to ask insightful questions about our tech stack and business goals.

The compensation insights above provide a realistic view of what you can expect for Data Engineering roles at Bestow. Keep in mind that total compensation often includes a mix of base salary, equity, and benefits, with variations based on your specific level of seniority and location.

You have the skills and the experience to succeed in this process. Continue to refine your technical fundamentals, practice articulating your architectural decisions, and review additional insights on Dataford to ensure you are fully prepared. Good luck with your preparation, and we look forward to speaking with you!