What is a Data Engineer at Flatiron Health?
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Curated questions for Flatiron Health from real interviews. Click any question to practice and review the answer.
Design a batch ETL pipeline that detects, imputes, and monitors missing values before loading analytics tables with daily SLA compliance.
Design a batch data pipeline with quality gates, quarantine handling, and monitored reprocessing for 120M finance records per day.
Design Terraform-based infrastructure as code for AWS data pipelines with reusable modules, secure state management, CI/CD, and drift control.
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Sign up freeAlready have an account? Sign inGetting Ready for Your Interviews
Preparation for your interviews should focus on demonstrating both your technical proficiency and your alignment with Flatiron Health’s values. Below are key evaluation criteria that interviewers will be focusing on:
Role-related Knowledge – You will need to showcase your understanding of data engineering principles, including data modeling, ETL processes, and the use of SQL and other programming languages. Interviewers will assess your ability to apply this knowledge to real-world scenarios.
Problem-Solving Ability – Your approach to challenges will be scrutinized. Interviewers want to see how you analyze problems, structure your thought process, and arrive at solutions. Be prepared to walk through your reasoning and decision-making.
Leadership and Collaboration – Your ability to work effectively in teams and communicate with stakeholders is crucial. Demonstrating strong interpersonal skills and a collaborative mindset will be key to showcasing your fit within the company culture.
Culture Fit / Values – Flatiron Health values individuals who are mission-driven and passionate about improving healthcare. Be prepared to articulate how your personal values align with the company’s mission and culture.
Interview Process Overview
The interview process for a Data Engineer at Flatiron Health typically involves several stages designed to assess both technical and interpersonal skills comprehensively. Initially, you will likely complete a take-home technical assessment focused on SQL and data manipulation. This will be followed by a video interview where you discuss your past experiences and perform live coding tasks.
Candidates can expect a rigorous selection process with multiple rounds, including technical interviews, behavioral assessments, and case studies. The emphasis is on both technical expertise and how well you fit into the collaborative environment of Flatiron Health. Throughout the process, you should demonstrate a clear understanding of data challenges in healthcare and a commitment to the company's mission of improving patient outcomes.
This visual timeline highlights the stages of the interview process, including initial assessments and interview rounds. Use this to plan your preparation effectively and manage your energy across different phases.
Deep Dive into Evaluation Areas
As you prepare for your interviews, it’s important to understand the areas where you will be evaluated. Here are some major evaluation areas to focus on:
Technical Skills
Technical proficiency is paramount for a Data Engineer. You will be evaluated on your ability to manipulate and analyze data, write efficient SQL queries, and leverage programming languages such as Python.
- SQL Proficiency – Expect to answer questions about complex queries, joins, and subqueries.
- Python Skills – Be prepared to demonstrate your ability to manipulate data using Python libraries such as Pandas.
Problem-Solving and Analytical Thinking
Your analytical skills and problem-solving abilities will be tested through case studies and practical challenges.
- Data Analysis – You may be asked to analyze datasets and derive insights.
- Scenario-Based Questions – Prepare to discuss how you would approach hypothetical data engineering problems.
Communication and Collaboration
The ability to effectively communicate and work as part of a team is critical. Interviewers will look for examples of how you have engaged with others to achieve common goals.
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Behavioral Questions – Be ready to discuss past experiences that highlight your teamwork and communication skills.
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Feedback Reception – Illustrate how you have responded to feedback and adapted your approach based on team input.
Domain Knowledge
A solid understanding of the healthcare domain will be beneficial, as you will be working with datasets related to patient care and outcomes.
- Healthcare Data Challenges – Be prepared to discuss specific challenges related to handling healthcare data, including privacy and data integrity.
Advanced Concepts
Some candidates may encounter questions on less common but valuable topics:
- Data warehousing concepts
- Data governance and compliance
- Machine learning fundamentals as they relate to data engineering
