These questions are based on real interview experiences from candidates who interviewed at this company. You can practice answering them interactively on Dataford to better prepare for your interview.

Preparing for the Solutions Architect interview requires a shift in mindset. You are being vetted not just on can you build it, but should you build it and how will you explain it.

Here are the key evaluation criteria you must demonstrate:

Technical Fluency & Execution – You must demonstrate hands-on capability. Interviewers will test your ability to read and write code (typically SQL and Python) and work with APIs. You aren't expected to be a kernel developer, but you must be comfortable manipulating data, automating tasks, and understanding system architecture.

Architectural Design & Scalability – You need to show that you can design solutions that survive "Meta Scale." This involves understanding data flow, API rate limits, integration patterns, and how distinct systems (e.g., a client’s ERP and Meta’s Ads Platform) talk to each other reliably.

Client Empathy & Communication – This is a major differentiator. You will be evaluated on your ability to break down complex technical concepts for non-technical audiences. Interviewers look for candidates who can navigate ambiguity, manage stakeholder expectations, and drive consensus.

Meta Culture (The "Jedi" Aspect) – You will be assessed on how you handle conflict, how you collaborate, and your alignment with Meta’s values (e.g., "Move Fast," "Focus on Impact"). They want to see that you are a builder who takes ownership.

The interview process for a Solutions Architect at Meta is rigorous but efficient. Based on recent candidate data, the process typically spans 5 to 6 rounds, moving from initial screens to a full "loop." Meta is known for a relatively fast scheduling pace, though feedback is usually compiled only after the entire loop is complete to ensure a holistic review.

The process generally begins with a Recruiter Screen to align on your background and interest. This is followed by a Technical Screen (often video-based), which focuses on your resume deep-dive and a preliminary coding or SQL task. If you pass this, you move to the Onsite Loop (currently virtual). The loop is a marathon of back-to-back interviews covering coding, system design, business cases, and behavioral assessments.

What makes Meta’s process distinctive is the separation of the "technical" and "partnership" signals. You will likely face a dedicated "Peer" or "Cross-functional" interview designed to test how you work with sales or product teams. Expect the interviewers to be specific—they want to know exactly what you did in your past projects, not just what your team achieved.

Interpreting the Timeline: The timeline above illustrates the standard progression. Note that the Technical Screen is a "gatekeeper" round; you must pass the coding/SQL bar to proceed. The Onsite Loop is the most intensive phase, where you will switch contexts rapidly between writing queries, designing architectures, and solving business cases. Pace yourself—it is a mental marathon.

To succeed, you must prepare deeply for specific types of rounds. The following areas are consistently reported by candidates and aligned with the job requirements.

Technical Proficiency (Coding & Data)

This is the area where many "consulting-heavy" candidates fail. You cannot talk your way out of the coding round.

• Why it matters: You will be building prototypes, scripts, and data pipelines.
• Evaluation: Expect a mix of SQL and Scripting (Python/Java). The difficulty is generally "Medium"—practical data manipulation rather than abstract algorithmic puzzles.
• Strong performance: You write clean, executable code. You handle edge cases (null values, duplicates). You can write complex SQL joins and window functions without hesitation.

Be ready to go over:

• SQL Data Manipulation: Joins (Inner, Left, Outer), Aggregations (GROUP BY, HAVING), and Window Functions (RANK, LEAD/LAG).
• Scripting: Parsing JSON/CSV files, hitting APIs, and transforming data structures (dictionaries/hash maps).
• API Usage: Understanding HTTP methods, authentication (OAuth), and error handling.

Example questions or scenarios:

• "Given two tables, Users and Ad_Spend, write a query to find the top 3 spenders per region."
• "Write a Python script to parse a nested JSON object representing an ad campaign structure and flatten it into a CSV format."
• "How would you debug a script that is failing to authenticate with an API?"

System Design & Integration

This round tests your architectural thinking.

• Why it matters: You will guide clients on how to integrate their tech stack with Meta.
• Evaluation: You will be given a vague problem and asked to design a solution.
• Strong performance: You ask clarifying questions first. You draw diagrams. You discuss trade-offs (e.g., real-time vs. batch processing) and consider constraints like API rate limits.

Be ready to go over:

• Integration Patterns: Webhooks vs. Polling, Batch vs. Real-time API calls.
• Scalability: Handling high volumes of data (e.g., pixel events).
• Data Modeling: Designing a schema for a specific business problem (e.g., Supply Chain inventory or Ad attribution).

Example questions or scenarios:

• "Design a system that syncs a client's offline purchase data with Meta's Conversion API."
• "A client wants to visualize their supply chain inventory in real-time. How would you architect the data flow from their ERP to our dashboard?"
• "How would you handle API rate limiting when migrating millions of records?"

Business Case & Consulting

This round tests your product sense and client-facing skills.

• Why it matters: You need to understand the business goal behind the technical request.
• Evaluation: You are presented with a client scenario and must propose a solution strategy.
• Strong performance: You identify the root cause, not just the symptom. You propose a phased approach (MVP first). You communicate clearly and persuasively.

Example questions or scenarios:

• "A large e-commerce client is seeing a drop in ad performance. How do you troubleshoot this?"
• "A client wants to implement a feature that we don't support. How do you handle this conversation?"
• "Prioritize these three client requests based on potential business impact and technical feasibility."

As a Solutions Architect at Meta, your day-to-day work is dynamic and split between deep technical work and high-level strategy.

Technical Consulting & Implementation: You act as the primary technical advisor for Meta’s partners (external clients) or internal stakeholders (e.g., for Enterprise Products). This involves walking them through technical documentation, helping them debug integration issues, and reviewing their code or architecture to ensure it meets Meta’s standards. For example, you might guide a major retailer on how to implement the Conversions API to improve their ad targeting.

Solution Building & Prototyping: You don't just advise; you build. You will frequently break down coding projects into tasks and partner with engineering teams. This might involve writing Python scripts to automate a workflow, building a proof-of-concept application to demonstrate a new feature, or configuring complex modules in supply chain platforms like o9 Solutions.

Cross-Functional Collaboration: You are the feedback loop. You work closely with Sales to unlock new opportunities by understanding client business goals. Simultaneously, you channel feedback from the field back to the Product and Engineering teams to influence the roadmap. You are expected to influence decision-making through data-centric presentations, often presenting to executive-level audiences at industry conferences or internal reviews.