An Engineering Manager at Spring Health plays a pivotal role in scaling a world-class mental health platform that directly impacts lives. Spring Health operates on a B2B2C model, delivering personalized mental healthcare benefits to employees of major enterprises. In this role, you are not just managing developers; you are building and optimizing systems that match members with the right providers, leverage clinical AI to predict treatment outcomes, and handle sensitive health data at scale.

As an Engineering Manager, your impact is felt across three key pillars: people, technology, and product delivery. You will lead cross-functional teams of software engineers, data scientists, and machine learning specialists to build robust, HIPAA-compliant products. Because Spring Health relies heavily on data-driven personalization, your team's work will directly influence clinical outcomes, making your engineering decisions highly consequential to the business and its members.

This role is highly collaborative and fast-paced. You will partner closely with Product Managers, Clinical Directors, and business stakeholders to translate a rapidly evolving roadmap into high-quality engineering deliverables. Successful Engineering Managers at Spring Health thrive in a lean, adaptable startup environment, possess deep technical empathy, and are passionate about solving complex engineering challenges in the healthcare space.

The questions you will face during the Spring Health interview process are designed to evaluate your technical breadth, leadership philosophy, and ability to execute in a fast-growing environment. While these questions are representative of past interview experiences, they are structured to test your underlying thought process rather than your ability to memorize specific answers.

Technical & Data Architecture

These questions evaluate your data literacy, system design capabilities, and understanding of relational databases. Even in management roles, Spring Health expects leaders to guide technical decisions effectively.

• Design a database schema for a restaurant reservation system, and write a SQL query to find the most popular dining hours.
• How would you structure a data model to track member engagement over time across multiple clinical interventions?

To succeed in the Spring Health interview process, you must demonstrate a balance of technical competence, leadership maturity, and product-minded execution. Your preparation should focus on showing how you can add value to a fast-scaling healthcare technology company.

Technical Breadth & Data Literacy – You must be comfortable discussing data models, relational database design, and SQL. For teams working with data science, having a solid grasp of machine learning lifecycles and production deployment is critical.

Execution in Ambiguity – Spring Health is a fast-growing company where priorities can shift rapidly. You need to show that you can establish clear processes, manage technical debt, and deliver results even when product requirements are fluid.

People-First Leadership – Interviewers want to see that you are an empathetic leader who prioritizes career development, psychological safety, and team health. Be prepared to discuss your management framework and how you build trust.

Cross-Functional Collaboration – You will interact with product managers, data scientists, clinicians, and business operations. You must demonstrate the ability to translate technical concepts for non-technical partners and negotiate priorities effectively.

The interview process at Spring Health is comprehensive and typically spans 4 to 6 weeks, though highly specialized roles or scheduling constraints can extend this timeline. The company places a strong emphasis on cross-functional alignment, meaning you will meet not only engineering leaders but also product managers and other key stakeholders.

The process begins with a standard recruiter screen to assess your background and alignment with the role. This is followed by a technical screen, which often combines a SQL/data modeling exercise with product design discussions. If you pass this stage, you will enter a comprehensive loop that evaluates your leadership, system design, and collaborative skills.

The visual timeline above outlines the typical progression from the initial application to the final offer stage. Candidates should use this timeline to pace their preparation, focusing heavily on SQL and system design in the early stages, while saving behavioral and cross-functional scenario preparation for the virtual onsite loop.

Data Modeling & SQL

This technical round evaluates your ability to structure data logically and write efficient queries. Spring Health relies on clean, structured data to drive clinical insights, making this a foundational competency for engineering leaders.

Be ready to go over:

• Relational Database Design – How to design normalized schemas, define primary and foreign keys, and establish relationships between entities.
• SQL Query Optimization – Writing complex queries involving joins, aggregations, subqueries, and window functions.
• Data Warehousing Concepts – Understanding how transactional databases interface with analytical data warehouses.
• Advanced concepts (less common) – High-performance indexing strategies, partitioning, and managing schema migrations at scale without downtime.

Example scenarios:

• Designing a database schema to track patient appointments, provider availability, and clinical survey results.
• Writing a SQL query to calculate the average time it takes for a member to book their first therapy session after signing up.

Machine Learning & Product Design

For engineering management roles overseeing data science or personalization teams, this round tests your ability to design scalable, production-grade machine learning systems that solve business problems.

Be ready to go over:

• End-to-End ML Lifecycle – Data collection, feature engineering, model training, evaluation, deployment, and monitoring.
• Causal Inference & Experimentation – Understanding how to measure the real-world impact of product changes using A/B testing and causal modeling.
• Model Evaluation Metrics – Choosing the right metrics (e.g., precision, recall, F1-score, ROC-AUC) based on the clinical and business context.
• Advanced concepts (less common) – Online vs. offline feature stores, handling cold-start problems in recommendation engines, and feedback loops.

Example scenarios:

• Designing a recommendation system that matches members with therapists based on demographic preferences, clinical needs, and therapist specialty.
• Explaining how you would validate and monitor a model designed to predict member churn from the platform.

People Leadership & Team Health

This behavioral round focuses on your management philosophy, coaching style, and how you foster a healthy, productive engineering team.

Be ready to go over:

• Performance Management – Having difficult conversations, setting clear expectations, and managing performance improvement plans (PIPs).
• Career Growth & Mentorship – Helping engineers progress from junior to senior levels and keeping senior ICs engaged.
• Conflict Resolution – Navigating interpersonal conflicts or deep technical disagreements within the team.
• Advanced concepts (less common) – Designing equitable hiring rubrics, managing distributed or remote-first teams, and building succession plans.

Example scenarios:

• Discussing a time when you had to deliver tough feedback to an engineer who believed they were performing exceptionally well.
• Explaining how you handled a situation where a key senior engineer threatened to leave the company.

Stakeholder Management & Prioritization

This round evaluates your execution skills and how you partner with product managers, designers, and business teams to deliver software.

Be ready to go over:

• Agile Delivery & Planning – Running sprint planning, managing backlogs, and estimating delivery timelines accurately.
• Managing Changing Priorities – Keeping your team focused and motivated when business goals pivot mid-sprint.
• Saying No & Negotiating – Managing expectations with non-technical stakeholders who request ad-hoc features or data pulls.
• Advanced concepts (less common) – Defining and tracking team SLAs, managing vendor integrations, and cross-team dependency mapping.

Example scenarios:

• Handling a high-priority request from the sales team to build a custom reporting feature for a major enterprise client on a tight deadline.
• Walk through how you align your team's technical debt reduction goals with the product team's feature roadmap.