Recently, I participated in a Solution Engineer technical interview that focused less on memorising answers and more on real-world problem-solving. After thinking through how I presented my solutions and walked through my problem-solving process, I realised I would have benefited from having a clear format to rehearse during my interview prep. That realisation is what inspired this blog post.

Before I dive into the interview experience itself, I want to briefly cover the Solution Engineer role specifically, how it differs from a Software Engineer. While both roles require strong technical skills, they differ in focus, responsibilities, and how success is measured. Understanding this distinction helps set the right context for how Solution Engineer interviews are structured and what interviewers are really evaluating.

What Is a Solution Engineer (or Professional Services Engineer)?

A Solution Engineer (often called a Professional Services Engineer in SaaS companies) is a technical, customer-facing role responsible for turning a product into a working solution for real businesses. Rather than building core product features, the role focuses on configuring platforms, integrating systems, modelling data, and designing workflows that help customers succeed quickly and at scale.

Solution Engineers work closely with Customer Success, Implementation, and Support teams to understand business requirements and translate them into practical, scalable technical solutions. This can include configuring workflows, building API integrations, setting up reporting, implementing billing logic, and validating data using SQL.

What makes this role unique is its blend of technical depth and real-world impact. Solution Engineers often operate in industries that are newly adopting software, where clarity, trust, and execution matter just as much as technical correctness. Success in this role depends not only on technical skill but also on the ability to communicate clearly, manage ambiguity, and guide customers through complex technical decisions.

Why Software Engineers Can Thrive as Solution Engineers

In my opinion, a Software Engineer who enjoys working closely with customers and is motivated to build solutions around real user needs could excel in this role. Strong communication skills are important—but hear me out. If you have a clear framework or template to guide requirement-gathering conversations, even someone more introverted can succeed as a Solution Engineer. Having a structure to follow builds confidence, keeps conversations focused, and makes complex discussions much easier to navigate.

This is something I’m actively working on myself. While communication doesn’t always come naturally, I take pride in solving problems that truly matter to users. There’s a unique satisfaction in delivering a solution that directly improves how someone works or runs their business.

With the rise of AI, I actually believe this role becomes even more valuable. While AI can assist with automation and analysis, it can’t replace human problem-solving, especially the ability to understand context, navigate ambiguity, and design solutions tailored to real people. This is where a Solution Engineer’s technical skills and judgment truly stand out.

Solution Engineer vs. Software Engineer

Although Solution Engineers and Software Engineers share a similar technical foundation, their responsibilities and success metrics differ in meaningful ways.

A Software Engineer primarily focuses on building and maintaining the product itself. Their work involves writing application code, designing internal systems, fixing bugs, and improving performance. Success is typically measured by code quality, feature delivery, and system reliability.

A Solution Engineer, on the other hand, focuses on how the product is applied in the real world. Instead of shipping product features, they design and deliver customer-specific solutions using existing platform capabilities. This includes configuring workflows, integrating third-party tools, modelling data with SQL, and ensuring that solutions align with both technical constraints and business goals.

Because of this, Solution Engineers are often evaluated on:

• Their ability to translate business needs into technical solutions
• Comfort working with APIs, SQL, and data models
• Clear communication with non-technical stakeholders
• Managing ambiguity, scope, and trade-offs
• Delivering repeatable, scalable implementations

Core Skill Set for a Solution Engineer

To succeed in a Solution Engineer or Professional Services Engineer role, you need a mix of technical execution and structured problem-solving. Based on my interview experience, these skills consistently came up:

• SQL & Data Modelling
  Ability to write queries to extract, validate, and transform data. As well as build reporting views that answer business questions.
• APIs, JSON, and Webhooks
  Understanding how data flows between systems through POST requests, JSON payloads, and webhook-based events.
• Problem-Solving & System Thinking
  Breaking down ambiguous requests, identifying edge cases, and designing scalable solutions rather than one-off fixes.
• Reporting & Analytics
  Translating raw data into meaningful reports that customers can actually use to track performance and outcomes.
• Communication & Requirement Gathering
  Asking the right questions, clarifying goals, and aligning technical decisions with business needs.

Example of a Solution Engineer Interview-Style Technical Scenario

For confidentiality reasons, I have created a question in a style similar to the one used in the interview.

The Scenario

A home services company (e.g., cleaning, repairs, or maintenance) wants to track service requests submitted through their website and third-party partners. These requests help the business understand demand, response times, and completion rates, ultimately improving service quality and operational efficiency.

The company currently receives service requests via a POST API endpoint. Each request includes customer details and request metadata.

However, they are facing several issues:

• Duplicate customer records are being created.
• Request statuses are not consistently tracked over time.
• They cannot accurately measure response times or completion rates.
• Reporting queries are slow and unreliable.
• They are unsure whether their current schema supports future growth.

The company wants help with:

• Improve data ingestion and integrity.
• Ensure requests are correctly associated with customers.
• Track request status changes over time.
• Design reporting that provides accurate operational insights.
• Ensure the system scales as the request volume grows.

Your role as a Solution Engineer is to analyse the current system, gather additional requirements, propose improvements, and design a solution to track service requests effectively.

Provided Context

Incoming Data

• Service request events arrive via a POST API endpoint as a JSON payload.
• Requests may be created before a job is scheduled.
• The payload contains customer information and request metadata.

Example JSON Payload:

{
  "request_id": "REQ-12345",
  "submitted_at": "2026-01-10T14:32:00Z",
  "service_type": "Plumbing",
  "priority": "High",
  "customer": {
    "customer_id": "C-9876",
    "name": "Jane Doe",
    "email": "jane@example.com"
  }
}

Current Database Tables

customers

Column	Description
customer_id	Unique identifier for each customer
name	Customer’s full name
email	Customer email address

service_requests

Column	Description
request_id	Unique identifier for the request
customer_id	ID of the associated customer
service_type	Type of service requested
priority	Priority level of the request
submitted_at	Timestamp of request submission
status	Current status of the request (e.g., pending, scheduled, completed)

Customer Session Exercise

As part of the interview, you will conduct a brief discovery session with the customer (interviewer) to gather additional information needed to improve the system.

Follow-Up

After gathering requirements, propose a solution including:

1. Updated database schema (if additional fields are needed).
2. Changes to the existing API endpoint (if more data needs to be ingested).
3. Data validation or transformation logic.
4. Reporting approach to evaluate service performance metrics, such as response times, request volume by service type, or customer satisfaction trends.
5. Optimise existing reporting queries ( if needed)

Solution Engineer Interview Practice Template

This template is structured to guide your customer session, solution design, and reporting/thought process for any scenario.

Pre-Interview Mental Checklist (2 minutes)

Before you start, identify:

[ ] Problem Type:
    - Data quality/integrity issue?
    - Performance/scale issue?
    - Integration/connectivity issue?
    - Reporting/analytics issue?
    - Security/compliance issue?

[ ] Customer Type:
    - Technical (Dev/Eng) → Go deeper on architecture
    - Business (Product/Ops) → Focus on outcomes
    - Executive → Focus on ROI/risk

[ ] Constraints to Probe:
    - Time: How urgent?
    - Budget: Build vs. buy?
    - Resources: Who maintains this?
    - Scale: Current vs. future volume?

Part 1: Customer Discovery Session

Goal: Understand requirements, workflow, and technical constraints.

1. Introduction
   • Introduce yourself: “I’m reviewing your system integration to understand your workflow and identify gaps.”
   • Set expectations: “I’ll ask about your data, users, and reporting needs.”
2. Data Requirements
   • Customer Data
   • Event / Request Data
   • Integration Data: submission method, authentication, API limits.
3. Workflow & Status
   • How requests flow: stages, approvals, SLA rules.
   • Priority handling, categorisation, or exceptions.
4. Reporting & KPIs
   • Metrics: volume, response/completion times, workload, SLA compliance.
   • Frequency: real-time, daily, weekly, monthly.
5. Constraints & Edge Cases
   • API limitations, rate limits, authentication, multi-location support, and duplicates.
   • Error handling and privacy/compliance needs.
6. Summarize & Confirm
   • Repeat key points for validation.

Part 2: Solution Design

Use the “3-Layer Proposal” structure:

Layer 1: Data Model (8 min)

Template:

"Based on what you've shared, here's how I'd structure the data..."

Proposed Changes:
✅ Add: [new fields to existing tables]
✅ Create: [new tables with purpose]
✅ Remove/Deprecate: [redundant or problematic fields]

Key Design Decisions:
1. [Why this solves the duplicate issue]
2. [Why this enables the reporting you need]
3. [Why this scales to your volume]

Layer 2: Data Flow / Integration (8 min)

Template:

"Here's how data would flow through the system..."

[Draw simple flow diagram]

API/Ingestion Layer:
- Accept: [format, authentication]
- Validate: [3 key validation rules]
- Transform: [normalization logic]

Processing Layer:
- Check: [duplicate detection logic]
- Enrich: [computed fields, lookups]
- Store: [which tables get updated]

Output Layer:
- Notify: [webhooks, events]
- Sync: [downstream systems]

Example Flow:

Request → Validate → Match Existing Customer → 
Create/Update Record → Log History → Trigger Workflow
         ↓
    [Return Error if invalid]

Layer 3: Reporting & Optimisation (9 min)

Template:

"For reporting, I'd recommend a two-tier approach..."

Tier 1: Real-Time Operational Queries
- Purpose: [Dashboard, alerts]
- Implementation: [Indexed views, materialized CTEs]
- Example: [Write 1-2 SQL queries]

Tier 2: Analytical Reporting
- Purpose: [Weekly reports, trends]
- Implementation: [Scheduled aggregations, summary tables]
- Example: [Describe structure]

Performance Optimizations:
✓ Index on: [columns]
✓ Partition by: [date/region]
✓ Archive: [old data strategy]

How to Solve the Example Scenario Using the Provided Template

Part 1: Discovery (What I Would Ask)

Even if some details are provided, I would still clarify.

Customer Integrity

• Are customers uniquely identified by customer_id from partners?
• Can the same email submit multiple requests?
• Are third-party partners trusted to send valid IDs?

This determines whether:

• We trust external IDs
• We generate our own IDs
• We deduplicate using email

Workflow & Status

• What are the valid statuses?
• Can requests move backwards? (e.g., scheduled → pending)
• Is SLA measured from submission to first response?
• Is a response defined as an assignment? scheduling? contact?

This determines how we calculate response time.

Reporting

• Do dashboards need to be real-time?
• How many requests per day?
• How long is historical data retained?

This determines whether:

• We use indexes only
• We need materialized views
• We need summary tables
• We need partitioning

Now I move to solution design.

Part 2: Layer 1 — Data Model Design

I would say:

“Based on your challenges, I would restructure the schema to explicitly separate identity, workflow tracking, and reporting optimization.”

1️⃣ Fixing Duplicate Customers

Problem:

Duplicate customers are being created.

Root Cause:

No enforced uniqueness + no upsert logic.

Solution:

Customers Table

customers
- id (internal PK)
- external_customer_id (nullable)
- name
- email (UNIQUE)
- phone
- created_at
- updated_at

Design Decisions:

• Add a UNIQUE constraint on email
• Use UPSERT logic during ingestion
• Treat external IDs as optional, not primary identity

In interview language:

“I would not trust partner-provided IDs blindly. I’d use email as a uniqueness constraint and implement upsert logic to prevent duplicate creation.”

2️⃣ Fixing Status Tracking

Problem:

Status is not consistently tracked over time.

Root Cause:

Only the current status is stored.

Solution: Add Status History Table

service_requests
- id
- customer_id (FK)
- service_type
- priority
- submitted_at
- current_status
- scheduled_at
- completed_at
- created_at
- updated_at

request_status_history
- id
- request_id (FK)
- old_status
- new_status
- changed_at
- changed_by

Why This Matters

Now we can:

• Calculate the time from submitted → assigned
• Calculate the time from assigned → completed
• Audit status changes
• Debug workflow issues

In interview language:

“To accurately measure response time and completion rate, we need immutable status history. Storing only current status prevents historical SLA analysis.”

That sounds senior.

3️⃣ Normalisation for Reporting Consistency

Instead of free-text fields:

service_types
priorities
statuses

Why?

• Prevent typos (“High”, “HIGH”, “high”)
• Improve reporting performance
• Allow future configuration changes

Part 3: Layer 2 — Data Flow / Integration

Now I’d describe the ingestion pipeline.

API Flow

Incoming Request → Validation → Deduplication → Insert → Status Log → Response

Step 1: Validation

• Required fields present
• Email format valid
• service_type exists in the lookup table
• Priority is a valid enum

Return 400 if invalid.

Step 2: Customer Matching Logic

Pseudo logic:

IF email exists:
    update customer if needed
ELSE:
    create customer

This prevents duplicates.

Step 3: Create Service Request

Insert into service_requests.

Default:

current_status = 'pending'

Step 4: Log Status History

Immediately insert into:

request_status_history
old_status = NULL
new_status = 'pending'
changed_at = submitted_at

Now every request starts with a tracked state.

Why This Design Scales

• Idempotent ingestion (prevent duplicates)
• Clear separation of identity and workflow
• Status tracking supports metrics
• Can later introduce event-driven architecture if volume increases

Part 4: Layer 3 — Reporting & Optimisation

Now I explicitly address the performance problem.

Problem: Reporting Queries Are Slow

Likely Causes:

• No indexes
• Large table scans
• Calculating metrics on raw data repeatedly

Solution Strategy

1️⃣ Indexing

INDEX ON service_requests(customer_id)
INDEX ON service_requests(current_status)
INDEX ON service_requests(submitted_at)
INDEX ON request_status_history(request_id, changed_at)

2️⃣ Response Time Calculation

Example:

SELECT 
    r.id,
    MIN(CASE WHEN h.new_status = 'assigned' THEN h.changed_at END) 
        - r.submitted_at AS response_time
FROM service_requests r
JOIN request_status_history h ON r.id = h.request_id
GROUP BY r.id;

Now we can compute SLAs properly.

3️⃣ Analytical Optimisation

If volume grows:

• Create a daily summary table:

daily_request_metrics
- date
- service_type
- total_requests
- avg_response_time
- completion_rate

Updated via scheduled job.

This prevents heavy aggregation on live tables.

4️⃣ Scalability Considerations

If volume grows significantly:

• Partition service_requests by month on submitted_at
• Archive completed requests older than X months
• Move analytics to the data warehouse if needed

In interview language:

“I’d start with indexing and proper normalization. If scale becomes significant, I’d introduce partitioning and potentially move heavy analytics to a separate reporting layer.”

Explicit Problem → Solution Mapping (Very Important in Interview)

Original Problem	Solution
Duplicate customers	Unique email + upsert logic
Status not tracked	request_status_history table
Cannot measure response time	Use status timestamps
Slow reporting	Indexing + summary tables
Unsure about scale	Partitioning + archival strategy

It shows closure.

How to Close the Interview Answer

I would finish with:

“To summarize:

• I clarified data identity rules to prevent duplication.
• I separated current state from historical tracking.
• I designed ingestion logic to be idempotent.
• I optimized reporting with indexing and optional summary tables.
• And I considered future scale through partitioning and analytics separation.

• Schema Design
  • customers: id, name, email, phone, address, membership_tier
  • service_requests: id, customer_id, type, priority, submitted_at, scheduled_at, status, assigned_tech, location, notes
• API / Integration
  • The POST endpoint accepts a JSON request with customer information.
  • If a new customer creates a customers table first.
  • Authentication via API token.
• Business Logic
  • Validate required fields: customer_id, service_type, priority, submitted_at
  • Normalise status: pending, assigned, in_progress, completed, cancelled

Thank you for reading. I would love to hear feedback about your Solution Engineer Interview and any preparation tips.