We Are Apt Data Engineer interview questions & guide 2026

The questions below represent the patterns observed in recent We Are Apt interviews. While specific technical challenges may shift based on project needs, these categories reflect the core competencies the team prioritizes during the evaluation process.

Technical Coding & Algorithms

These questions test your proficiency in Python and your ability to write clean, efficient code under time constraints. Focus on your problem-solving process rather than just reaching the final answer.

• Solve two medium-difficulty Data Structures and Algorithms (DSA) problems using Python.
• Given an array, implement a function to optimize data retrieval based on specific constraints.

Preparation at We Are Apt should be structured around demonstrating both your technical depth and your ability to communicate complex ideas to non-technical stakeholders. Do not simply memorize syntax; focus on the why behind your architectural decisions.

Technical Proficiency – You must be comfortable writing production-ready Python code and handling complex SQL queries. Be prepared to explain the underlying logic of your code and how it scales under heavy load.

Architectural Thinking – You will be evaluated on your ability to design end-to-end data solutions. Focus on understanding the Azure ecosystem, specifically Azure Databricks and Microsoft Fabric, as these are central to the firm's current stack.

Communication & Stakeholder Management – The best candidates can bridge the gap between engineering and business. Practice articulating how your technical solutions solve specific problems for legal or financial teams.

The interview process at We Are Apt is designed to be rigorous yet collaborative. It typically begins with a technical screening, often followed by a series of rounds that test your coding ability, system design intuition, and project experience. You should expect a pace that moves quickly; the team values candidates who can demonstrate high technical competency without needing constant guidance.

The firm emphasizes a "hands-on" philosophy. You will likely spend a significant portion of your time in interviews working through live problems in an online editor, so ensure you are comfortable writing code in a collaborative, remote environment. Throughout the process, interviewers look for a balance of technical expertise and the ability to articulate your thought process clearly.

The visual timeline above illustrates the progression from initial technical assessments to deeper, project-based discussions. Candidates should treat each stage as an opportunity to demonstrate both hard skills and a collaborative mindset, as the interviewers prioritize team-fit as much as technical output.

Technical Depth and Coding

You must demonstrate mastery of Python and efficient algorithmic thinking. Strong performance here means writing clean, readable, and optimized code that considers edge cases.

• DSA proficiency – Be ready to solve problems of medium difficulty.
• Code quality – Focus on modularity and naming conventions.
• Complexity analysis – Always be prepared to discuss the Big O complexity of your solutions.

System Design and Architecture

This area evaluates your ability to build platforms that handle enterprise-scale data. You should be prepared to discuss the full lifecycle of a data project.

• Cloud Infrastructure – Understanding of Azure services is critical.
• Data Warehousing – Experience with T-SQL and enterprise data management.
• Scalability – How your designs handle increasing data volumes and velocity.

Project Ownership and Communication

This is the "client-facing" component of the role. You need to show that you can manage a project from concept to completion and communicate progress to stakeholders.

• Lifecycle management – How you handle discovery, design, and deployment.
• Stakeholder interaction – Explaining technical debt or project risks to non-technical partners.
• Autonomy – Providing examples of when you took the lead on a complex initiative.

As a Data Engineer, you own the lifecycle of data initiatives. This includes everything from the initial discovery and design phases to the final deployment and maintenance of pipelines. You will be expected to utilize your knowledge of Azure Cloud Data Factory, Azure Databricks, and Microsoft Fabric to create solutions that are not only scalable but also align with the strategic goals of the legal and finance departments.

Collaboration is a daily requirement. You will work alongside legal ops and knowledge management teams to ensure that the data products you build—such as Power BI datasets and semantic models—directly support their operational objectives. Success in this role requires a proactive approach; you should be comfortable identifying bottlenecks in existing workflows and proposing innovative technical solutions to resolve them.

A strong candidate for this role possesses a blend of deep technical expertise and the maturity to handle a client-facing environment.

• Must-have skills:
  • 7+ years of professional experience in data engineering.
  • Deep expertise in Azure Databricks, Azure Cloud Data Factory, and Microsoft Fabric.
  • Advanced proficiency in SQL Server, T-SQL, and SSIS.
  • Proven ability to deliver projects through the full lifecycle.
• Nice-to-have skills:
  • Familiarity with legal data platforms (e.g., billing, matter management, document management systems).
  • Experience in the financial services (FS) industry.
  • Strong background in building and maintaining executive-level dashboards.

Q: How much time should I spend preparing for the coding rounds? A: You should aim to be comfortable solving medium-difficulty Python algorithmic problems. Dedicate time to practicing in an online editor to get used to writing code without an IDE's autocomplete features.

Q: Is there an HR or culture fit round? A: While there may not be a formal "HR" interview, every round is an opportunity to demonstrate your communication skills. The team values candidates who are collaborative, understandable, and cooperative.

Q: What is the most important thing to emphasize during the interview? A: Emphasize your ability to own a project end-to-end. The team looks for engineers who can operate autonomously and bridge the gap between technical implementation and business value.

Q: How much remote flexibility is there? A: The role is remote, but you are expected to be available during EST/CST hours to ensure effective collaboration with the team and stakeholders.

• Articulate your process: When solving coding problems, talk through your thought process out loud. Interviewers at We Are Apt care more about your problem-solving methodology than just the final result.
• Prepare your "Project Story": Have a clear, structured narrative for a complex project you have led. Focus on the challenges you faced and how you navigated them.
• Know the Stack: Be prepared to discuss specific use cases for Azure Databricks and Microsoft Fabric. Being able to explain why you chose a specific tool for a specific problem is a major differentiator.
• Be ready for technical depth: Don't shy away from discussing the details of database design or pipeline optimization; the interviewers expect a high level of technical rigor.

The Data Engineer position at We Are Apt is an exceptional opportunity to influence the data landscape within the legal and financial sectors. By focusing on your technical foundations in Python and Azure, and by demonstrating your ability to lead projects with autonomy, you will position yourself as a top-tier candidate. Remember that this role values those who can bridge the gap between complex engineering and strategic business impact.

To further refine your preparation, you can explore additional interview insights, practice questions, and comprehensive resources on Dataford. Stay focused, be clear in your communication, and trust in your technical experience. Your preparation will directly correlate to your confidence and success during the interview process.

The compensation data provided reflects the market range for senior-level data engineering roles within the firm. Candidates should interpret these figures as a guide based on experience and location, keeping in mind that total compensation packages may also include benefits and other performance-based incentives.