Metriport Software Engineer Interview Guide

The 60-second brief

A Software Engineer at Metriport is not just an individual contributor writing code; they are an entrepreneurial builder charged with solving one of healthcare's most persistent challenges: patient data interoperability. Metriport operates an open-source data intelligence platform that connects modern digital health applications with legacy healthcare IT systems, enabling real-time access and exchange of comprehensive medical data for over 300 million individuals. As a generalist engineer on this high-performing team, you will design, build, and scale systems that handle massive volumes of sensitive health data, directly impacting customers like Amazon One Medical, Strive Health, and Brightside Health. The engineering team at Metriport operates with high autonomy, minimal bureaucracy, and a strong hacker mentality.

This guide is scoped to a Software Engineer candidate at Metriport.

Focus areas

• Full-stack development
• Scalable systems
• Integration with healthcare IT systems

The Metriport interview process

Metriport candidates report 4 rounds over 3-5 weeks. The stages below are what candidates describe, not a published process.

Initial Conversation

Discussion with a founder or senior engineer to align on background, entrepreneurial drive, and mutual expectations.

What to demonstrate

• Discussion with a founder or senior engineer to align on background, entrepreneurial drive, and mutual expectations
• Depth in Full-stack development

How to prepare

• Answer aloud and timed: How would you design a system to ingest high-volume, real-time data from multiple external APIs with varying rate limits and reliability?
• Answer aloud and timed: Explain how you would structure a database schema to store highly nested, semi-structured clinical data (like FHIR resources) while ensuring fast query performance.

Hands-on Coding Assessment

Practical evaluation that reflects actual day-to-day coding tasks.

What to demonstrate

• Practical evaluation that reflects actual day-to-day coding tasks
• Depth in Full-stack development

How to prepare

• Answer aloud and timed: Describe how you would build a reliable webhook delivery system that guarantees at-least-once delivery to customers, even during downstream outages.
• Answer aloud and timed: How do you decide between using a relational database like PostgreSQL versus a NoSQL store like DynamoDB for a highly scalable microservice?

System Architecture Deep Dive

In-depth discussion and evaluation of system design and architecture skills.

What to demonstrate

• In-depth discussion and evaluation of system design and architecture skills
• Depth in Full-stack development

How to prepare

• Answer aloud and timed: How would you architect a system to process and index large volumes of unstructured medical text (e.g., doctor's notes) using LLMs, ensuring both cost-efficiency and low latency?
• Answer aloud and timed: Write a function to parse, clean, and validate an incoming payload containing legacy medical records, transforming it into a standardized JSON format.

Team Collaboration Evaluation

Assessment of problem-solving abilities and collaboration in a high-ownership, high-intensity environment.

What to demonstrate

• Assessment of problem-solving abilities and collaboration in a high-ownership, high-intensity environment
• Depth in Full-stack development

How to prepare

• Answer aloud and timed: How would you manage concurrency and rate-limiting when fetching data from dozens of external healthcare clinics simultaneously?
• Answer aloud and timed: Implement a robust error-handling wrapper for an asynchronous API client that handles retries with exponential backoff.

Where candidates lose points

Showcase your shipping velocity

During your interviews, emphasize projects where you took a feature from idea to production in days rather than weeks. Be ready to explain how you managed risk and cut scope to deliver value quickly.

Demonstrate a product-first mindset

When discussing technical designs, always tie your decisions back to the end user or customer. Explain how your architecture choices directly improve data reliability, latency, or feature delivery for healthcare organizations.

Metriport values competence over prestige

Focus on demonstrating your practical building capabilities and problem-solving skills rather than relying on pedigree or past company names.

Be prepared for ambiguity

In a fast-growing startup, requirements can change rapidly. Show that you are comfortable taking vague, high-level product goals and breaking them down into actionable technical steps without needing constant hand-holding.

Questions & practice

Write a function to parse, clean, and validate an incoming payload containing legacy medical records, transfor

Medium · Practical Coding & Data Wrangling

Write a function to parse, clean, and validate an incoming payload containing legacy medical records, transforming it into a standardized JSON format.

Approach

1. Restate the input: its shape, its size, and what is guaranteed about it.
2. Name the brute-force solution and its complexity before improving on it.
3. Choose the data structure from the access pattern, not from familiarity.
4. State the target complexity and say which constraint rules the naive version out.

Follow-up

• How does this change if the input no longer fits in memory?
• What is the worst case, and how likely is it on real data?

How would you manage concurrency and rate-limiting when fetching data from dozens of external healthcare clini

Medium · Practical Coding & Data Wrangling

How would you manage concurrency and rate-limiting when fetching data from dozens of external healthcare clinics simultaneously?

Approach

1. Say what the runtime actually does before reasoning about the code.
2. Name what is shared across threads and what owns each piece of state.
3. Identify the window where an invariant is briefly untrue.
4. Distinguish a value from a reference to it, and say which one you handed out.

Follow-up

• What happens if two callers reach this at the same time?
• Where could this allocate more than you expect?

How do you optimize a Node.js backend application that is experiencing CPU bottlenecks during heavy JSON seria

Medium · Practical Coding & Data Wrangling

How do you optimize a Node.js backend application that is experiencing CPU bottlenecks during heavy JSON serialization tasks?

Approach

1. Restate the input: its shape, its size, and what is guaranteed about it.
2. Name the brute-force solution and its complexity before improving on it.
3. Choose the data structure from the access pattern, not from familiarity.
4. State the target complexity and say which constraint rules the naive version out.

Follow-up

• How does this change if the input no longer fits in memory?
• What is the worst case, and how likely is it on real data?

Write a TypeScript utility type that deeply maps and transforms keys of a complex nested object from snake_cas

Medium · Practical Coding & Data Wrangling

Write a TypeScript utility type that deeply maps and transforms keys of a complex nested object from snake_case to camelCase.

Approach

1. Restate the input: its shape, its size, and what is guaranteed about it.
2. Name the brute-force solution and its complexity before improving on it.
3. Choose the data structure from the access pattern, not from familiarity.
4. State the target complexity and say which constraint rules the naive version out.

Follow-up

• How does this change if the input no longer fits in memory?
• What is the worst case, and how likely is it on real data?

Explain how you would structure a database schema to store highly nested, semi-structured clinical data (like

Medium · System Architecture & Data Pipelines

Explain how you would structure a database schema to store highly nested, semi-structured clinical data (like FHIR resources) while ensuring fast query performance.

Approach

1. Name the grain you start from and join outward from it.
2. Check whether any join is one-to-many before aggregating, or the sums inflate.
3. Say which index the query would use, and what makes it unusable.
4. Handle the rows that do not match: that is usually the actual question.

Follow-up

• How does the query change if that join becomes one-to-many?
• What happens to this when the table is ten times larger?

Denormalise tenant onto revisions and backfill it live

Hard · denormalisation · online migration · backfill

resource_revision (revision_id, resource_id, version, actor_user_id, change_kind, patch, request_id, created_at) has 400M rows and no tenant column; tenant_id lives only on resource. Two reads need it: a tenant-scoped audit feed ordered by created_at DESC, and an offboarding purge. Both join back to resource today. Justify adding tenant_id to resource_revision against those two reads, name the anomaly the copy introduces and the constraint that prevents it, then give the ordered migration for a live table taking 1.2k writes/second — the lock each step takes, how the backfill is batched, and where each step stops being reversible. PostgreSQL 16.

Approach

1. Justify from the access path rather than from taste. Without the column, the audit feed either scans resource_revision by created_at and discards other tenants' rows, or resolves the tenant's resource_ids first and probes with them — both proportional to the tenant's whole history rather than to one page. With (tenant_id, created_at DESC, revision_id DESC) it is a seek that stops at 50 rows, and the purge becomes a ranged delete instead of a join.
2. Name the cost exactly: a second copy of a fact can disagree with the first. Make the disagreement unwritable rather than documented — add UNIQUE (resource_id, tenant_id) on resource so it can serve as a foreign-key target, then FOREIGN KEY (resource_id, tenant_id) REFERENCES resource (resource_id, tenant_id) on the revision table. A revision can then only ever carry its parent's tenant.
3. Step one, expand: ALTER TABLE resource_revision ADD COLUMN tenant_id BIGINT NULL, with no default, so it is a catalogue change and no rewrite. It still needs ACCESS EXCLUSIVE for an instant, and that instant queues behind the longest open transaction on the table while every later query queues behind it — set lock_timeout to 2s and retry rather than wait.
4. Step two, dual-write: deploy the writer that populates tenant_id on every new revision while reads still use the join. Reversible by redeploying the previous build, because nothing reads the column yet.

Follow-up

• The backfill is half finished and a rollback is required. What state is the table in, and what does the previous build do with a half-populated column?
• How do you verify the backfill actually finished, given rows are still being inserted while it runs?

How would you design a system to ingest high-volume, real-time data from multiple external APIs with varying r

Medium · System Architecture & Data Pipelines

How would you design a system to ingest high-volume, real-time data from multiple external APIs with varying rate limits and reliability?

Approach

1. Fix the scope first: who calls this, how often, and what they do when it fails.
2. Name the read and write paths separately; they rarely have the same bottleneck.
3. Choose a partition key and say what query it makes expensive.
4. State the consistency you need, and where you are willing to be stale.

Follow-up

• What breaks first when traffic grows ten times?
• How does this behave when that dependency is down for an hour?

Describe how you would build a reliable webhook delivery system that guarantees at-least-once delivery to cust

Medium · System Architecture & Data Pipelines

Describe how you would build a reliable webhook delivery system that guarantees at-least-once delivery to customers, even during downstream outages.

Approach

1. Say who the caller is and what they do when the call fails halfway.
2. Define the identity of a request so a retry cannot double-apply it.
3. Separate accepted, pending, failed and confirmed; they are different facts.
4. Design the error taxonomy before the success shape; callers branch on it.

Follow-up

• What happens if the caller retries after a timeout?
• How does a client discover it is on an old version of this contract?

How do you decide between using a relational database like PostgreSQL versus a NoSQL store like DynamoDB for a

Medium · System Architecture & Data Pipelines

How do you decide between using a relational database like PostgreSQL versus a NoSQL store like DynamoDB for a highly scalable microservice?

Approach

1. Fix the scope first: who calls this, how often, and what they do when it fails.
2. Name the read and write paths separately; they rarely have the same bottleneck.
3. Choose a partition key and say what query it makes expensive.
4. State the consistency you need, and where you are willing to be stale.

Follow-up

• What breaks first when traffic grows ten times?
• How does this behave when that dependency is down for an hour?

How would you architect a system to process and index large volumes of unstructured medical text (e.g., doctor

Medium · System Architecture & Data Pipelines

How would you architect a system to process and index large volumes of unstructured medical text (e.g., doctor's notes) using LLMs, ensuring both cost-efficiency and low latency?

Approach

1. Fix the scope first: who calls this, how often, and what they do when it fails.
2. Name the read and write paths separately; they rarely have the same bottleneck.
3. Choose a partition key and say what query it makes expensive.
4. State the consistency you need, and where you are willing to be stale.

Follow-up

• What breaks first when traffic grows ten times?
• How does this behave when that dependency is down for an hour?

Implement a robust error-handling wrapper for an asynchronous API client that handles retries with exponential

Medium · Practical Coding & Data Wrangling

Implement a robust error-handling wrapper for an asynchronous API client that handles retries with exponential backoff.

Approach

1. Say who the caller is and what they do when the call fails halfway.
2. Define the identity of a request so a retry cannot double-apply it.
3. Separate accepted, pending, failed and confirmed; they are different facts.
4. Design the error taxonomy before the success shape; callers branch on it.

Follow-up

• What happens if the caller retries after a timeout?
• How does a client discover it is on an old version of this contract?

One customer endpoint stalls deliveries to every other destination

Medium · bulkhead · timeouts · saturation

The egress service delivers about 1.5k webhooks/second across 40,000 destinations, with a per-destination concurrency cap of 4 and a 10-second connect-plus-read timeout. Throughput falls to 300/second, queue depth climbs, and p99 delivery latency for unaffected destinations goes from 200 ms to minutes, while the error rate barely moves. One tenant holds 900 destination rows whose URLs share a hostname that now answers in 9.5 seconds. Explain the mechanism with the arithmetic, then give the containment in the order you would apply it.

Approach

1. Look at saturation before errors. A flat error rate with collapsing throughput says nothing is failing, things are waiting, so the first signal to pull is in-flight request count or pool wait time rather than the error counter. This is the distinction that decides the whole investigation.
2. Group in-flight work by resolved host, not by destination id. The cap is keyed per destination row, so 900 rows sharing one hostname buy 3,600 concurrent slots against a single host, each held for 9.5 seconds. The bulkhead was never a bulkhead for that host, and grouping by the wrong dimension is why the dashboard looked healthy.
3. Do the arithmetic in both directions. Required concurrency is arrival rate times latency, so 1.5k/second at 200 ms needs about 300 in flight, which is entirely consumed by 3,600 slow slots; conversely whatever concurrency is left sustains rate equals concurrency divided by 9.5 seconds, which is the 300/second you are seeing. Matching both numbers is what promotes this from a plausible story to the mechanism.
4. Explain why the circuit breaker never helped. It opens on consecutive failures, and a 9.5-second response inside a 10-second timeout is a success. Slow is not failing, so an error-rate breaker cannot see this; you need a slow-call ratio, a deadline propagated from the caller's remaining budget, or a concurrency limiter.

Follow-up

• The host recovers to 80 ms. How long does the queue take to drain, and what does the drain do to the recovered host?
• Where should the 10-second timeout number actually come from?

Suggested practice checklist

Built from the rounds and topics Metriport candidates report.

Day 1 — Map the Metriport loop

• Write out the reported sequence: Initial Conversation, Hands-on Coding Assessment, System Architecture Deep Dive, Team Collaboration Evaluation.
• For each round, write one sentence on what it is judging, from the description above, and mark the one you are least ready for.

Deliverable: A one-page map of the 4 reported rounds, with the weakest marked.

Day 2 — Work Full-stack development

• Spend the session on Full-stack development, which Metriport candidates report being tested on.
• Write one worked example in Full-stack development and time yourself on it.

Deliverable: One timed worked example in Full-stack development.

Day 3 — Work Scalable systems

• Spend the session on Scalable systems, which Metriport candidates report being tested on.
• Write one worked example in Scalable systems and time yourself on it.

Deliverable: One timed worked example in Scalable systems.

Day 4 — Work Integration with healthcare IT systems

• Spend the session on Integration with healthcare IT systems, which Metriport candidates report being tested on.
• Write one worked example in Integration with healthcare IT systems and time yourself on it.

Deliverable: One timed worked example in Integration with healthcare IT systems.

Day 5 — Answer out loud: System Architecture & Data Pipelines

• Answer aloud, timed: How would you design a system to ingest high-volume, real-time data from multiple external APIs with varying rate limits and reliability?
• Answer aloud, timed: Explain how you would structure a database schema to store highly nested, semi-structured clinical data (like FHIR resources) while ensuring fast query performance.

Deliverable: Spoken answers to 2 reported System Architecture & Data Pipelines question(s), under time.

Day 6 — Answer out loud: Practical Coding & Data Wrangling

• Answer aloud, timed: Write a function to parse, clean, and validate an incoming payload containing legacy medical records, transforming it into a standardized JSON format.
• Answer aloud, timed: How would you manage concurrency and rate-limiting when fetching data from dozens of external healthcare clinics simultaneously?

Deliverable: Spoken answers to 2 reported Practical Coding & Data Wrangling question(s), under time.

Day 7 — Answer out loud: Behavioral & Entrepreneurial Drive

• Answer aloud, timed: Tell me about a time you shipped a complex feature or product in a fraction of the time originally scoped. What trade-offs did you make?
• Answer aloud, timed: Describe a situation where you had to dive deep into a technical domain or codebase where you had zero prior experience. How did you ramp up and deliver?

Deliverable: Spoken answers to 2 reported Behavioral & Entrepreneurial Drive question(s), under time.

Behavioural preparation

Behavioural rounds judge the decision you made and what it cost.

Tell me about a time you shipped a complex feature or product in a fraction of the time originally scoped. Wha

Medium · Behavioral & Entrepreneurial Drive

Tell me about a time you shipped a complex feature or product in a fraction of the time originally scoped. What trade-offs did you make?

Approach

1. Pick a story where you made the decision, not one where you watched it.
2. State the situation in two sentences and spend the rest on the reasoning.
3. Give the blast radius: what could have broken, and what you measured.
4. Name the disagreement and how you resolved it with evidence.

Follow-up

• What would you do differently if you ran that again?
• How did you know your change caused the improvement?

Describe a situation where you had to dive deep into a technical domain or codebase where you had zero prior e

Medium · Behavioral & Entrepreneurial Drive

Describe a situation where you had to dive deep into a technical domain or codebase where you had zero prior experience. How did you ramp up and deliver?

Approach

1. Pick a story where you made the decision, not one where you watched it.
2. State the situation in two sentences and spend the rest on the reasoning.
3. Give the blast radius: what could have broken, and what you measured.
4. Name the disagreement and how you resolved it with evidence.

Follow-up

• What would you do differently if you ran that again?
• How did you know your change caused the improvement?

How do you balance the need for high-quality, maintainable code with the business urgency to ship features qui

Medium · Behavioral & Entrepreneurial Drive

How do you balance the need for high-quality, maintainable code with the business urgency to ship features quickly to customers?

Approach

1. Pick a story where you made the decision, not one where you watched it.
2. State the situation in two sentences and spend the rest on the reasoning.
3. Give the blast radius: what could have broken, and what you measured.
4. Name the disagreement and how you resolved it with evidence.

Follow-up

• What would you do differently if you ran that again?
• How did you know your change caused the improvement?

Tell me about a project you led end-to-end. How did you gather feedback, handle conflicting technical opinions

Medium · Behavioral & Entrepreneurial Drive

Tell me about a project you led end-to-end. How did you gather feedback, handle conflicting technical opinions, and ensure successful adoption?

Approach

1. Pick a story where you made the decision, not one where you watched it.
2. State the situation in two sentences and spend the rest on the reasoning.
3. Give the blast radius: what could have broken, and what you measured.
4. Name the disagreement and how you resolved it with evidence.

Follow-up

• What would you do differently if you ran that again?
• How did you know your change caused the improvement?

Why do you want to work in a high-intensity, in-office startup environment where team members work with high a

Medium · Behavioral & Entrepreneurial Drive

Why do you want to work in a high-intensity, in-office startup environment where team members work with high autonomy and minimal supervision?

Approach

1. Pick a story where you made the decision, not one where you watched it.
2. State the situation in two sentences and spend the rest on the reasoning.
3. Give the blast radius: what could have broken, and what you measured.
4. Name the disagreement and how you resolved it with evidence.

Follow-up

• What would you do differently if you ran that again?
• How did you know your change caused the improvement?