What is a Data Engineer at Aircall?

As a Data Engineer on the Data and Science team at Aircall, you are at the core of transforming raw telecommunications and interaction data into actionable business intelligence. Aircall is a leading cloud-based voice platform that seamlessly integrates with CRMs and support tools. Because voice and communication data are inherently complex, high-volume, and real-time, your work directly dictates how well Aircall can deliver advanced analytics, call routing insights, and machine learning capabilities to its global customer base.

Your impact in this position spans across multiple products and internal teams. You will be responsible for designing and scaling the data architecture that processes millions of daily call events, transcriptions, and metadata. By ensuring that this data is reliable, accessible, and well-modeled, you empower Data Scientists to build predictive models and enable Product teams to ship features like real-time sentiment analysis and advanced call metrics.

This role is highly strategic and technically rigorous. You will not just be moving data from point A to point B; you will be solving complex distributed systems problems, managing intricate ETL/ELT pipelines, and ensuring data governance at scale. Expect an environment that balances the agility of a fast-growing tech company with the engineering discipline required to handle mission-critical, high-availability data infrastructure.

Getting Ready for Your Interviews

Preparing for the Aircall interview requires a strategic blend of deep technical review and a strong understanding of product-driven data engineering. You should approach your preparation by focusing on the core competencies that the engineering team values most.

Technical Proficiency – You will be evaluated on your mastery of core data engineering tools, specifically advanced SQL, Python, and cloud data warehousing concepts. Interviewers want to see that you can write clean, optimized code and understand the underlying execution engines of the databases you use.

System and Pipeline Design – This measures your ability to architect scalable, fault-tolerant data ecosystems. You can demonstrate strength here by confidently discussing trade-offs between batch and streaming, choosing the right orchestration tools, and designing robust data models (like star or snowflake schemas) that serve complex business needs.

Problem-Solving and Debugging – Aircall values engineers who can navigate ambiguity. Interviewers will assess how you break down complex, open-ended problems, identify potential bottlenecks in data pipelines, and troubleshoot data quality issues in production environments.

Culture and Collaboration – Working on the Data and Science team means constant cross-functional interaction. You will be evaluated on your communication skills, your ability to translate business requirements into technical specifications, and your alignment with Aircall’s collaborative, transparent, and customer-centric culture.

Interview Process Overview

The interview process for a Data Engineer at Aircall is designed to be rigorous but highly collaborative. It typically begins with a recruiter screen to align on your background, expectations, and location requirements for the Seattle office. Following this, you will move into a technical screening phase, which usually involves a live coding or SQL session with a senior engineer. This step focuses heavily on your foundational skills and your ability to write clean, performant code under standard time constraints.

If you pass the technical screen, you will be invited to the virtual onsite loop. This comprehensive stage is where Aircall’s process distinguishes itself. You will participate in multiple specialized sessions, including a deep-dive system design interview, a data modeling scenario, and behavioral rounds with cross-functional stakeholders such as Data Scientists or Product Managers. The company places a strong emphasis on how you think about data quality and business value, rather than just testing your knowledge of specific syntax.

Throughout the process, you can expect interviewers to be engaged and conversational. They are looking for colleagues they can brainstorm with, so treating the interviews as collaborative working sessions will serve you well.

This visual timeline outlines the typical progression from the initial recruiter screen through the technical assessments and the final virtual onsite loop. Use this to pace your preparation, ensuring your foundational coding skills are sharp for the early rounds, while reserving time to practice high-level system design and behavioral narratives for the final stages. Nuances may exist depending on your seniority level, but the core technical and architectural evaluations remain consistent.

Deep Dive into Evaluation Areas

To succeed in the Aircall interview, you must demonstrate depth across several key pillars of data engineering. Below is a breakdown of the primary evaluation areas.

Data Modeling and Warehouse Design

Aircall deals with a massive influx of event-driven data, from call logs to CRM sync events. Interviewers want to know that you can structure this data efficiently for both analytical querying and machine learning applications. Strong performance means you can design schemas that minimize redundancy while maximizing query performance.

Be ready to go over:

• Dimensional Modeling – Understanding facts, dimensions, and the trade-offs between star and snowflake schemas.
• Data Partitioning and Clustering – Strategies to optimize query costs and performance in cloud data warehouses (e.g., Snowflake, BigQuery).
• Handling Slowly Changing Dimensions (SCDs) – Techniques for tracking historical data changes, particularly for user accounts or billing plans over time.
• Advanced concepts (less common) – Data vault modeling, cost-optimization strategies for specific cloud execution engines, and multi-tenant data architecture.

Example questions or scenarios:

• "Design a data model to track call metrics (duration, wait time, drop rate) across different geographic regions and customer accounts."
• "How would you handle late-arriving call transcription data in your daily reporting tables?"
• "Walk me through how you would optimize a slow-running query that joins a massive fact table with multiple large dimensions."

Data Pipeline Architecture (ETL/ELT)

You will be tasked with designing systems that move and transform data reliably. Aircall evaluators look for your ability to build fault-tolerant pipelines, manage dependencies, and ensure data freshness.

Be ready to go over:

• Batch vs. Streaming – Knowing when to use daily batch jobs versus real-time streaming (e.g., Kafka, Kinesis) for live call dashboards.
• Orchestration – Designing DAGs (Directed Acyclic Graphs) using tools like Airflow or Dagster, including error handling and retries.
• Data Quality and Testing – Implementing checks (e.g., using dbt tests or Great Expectations) to catch anomalies before they reach downstream consumers.
• Advanced concepts (less common) – Idempotency in pipeline design, backfilling strategies for massive historical datasets, and CDC (Change Data Capture) implementation.

Example questions or scenarios:

• "Design an ETL pipeline that extracts user data from a third-party CRM API, transforms it, and loads it into our warehouse."
• "Your daily Airflow job failed halfway through. How do you design the pipeline so that restarting it doesn't create duplicate records?"
• "How would you monitor and alert on data freshness for a critical executive dashboard?"

SQL and Python Proficiency

Your hands-on coding ability is critical. You must be able to manipulate data efficiently using SQL and write robust, modular Python code for API integrations and custom transformations.

Be ready to go over:

• Advanced SQL – Mastery of window functions, CTEs (Common Table Expressions), complex joins, and aggregations.
• Python Data Structures – Using dictionaries, lists, and sets efficiently, as well as understanding time complexity.
• API Interactions – Writing Python scripts to handle pagination, rate limiting, and authentication when pulling data from external sources.
• Advanced concepts (less common) – PySpark optimization, memory profiling in Python, and UDF (User Defined Function) performance tuning in SQL.

Example questions or scenarios:

• "Write a SQL query to find the top 3 longest calls per customer account for the current month."
• "Given a JSON payload of nested call metadata, write a Python function to flatten the data and extract specific nested fields."
• "Write a query to calculate the rolling 7-day average of dropped calls per agent."

Cross-Functional Collaboration and Impact

Aircall expects Data Engineers to partner closely with Data Scientists and Product teams. You will be evaluated on your ability to understand business context, push back on unrealistic requirements, and communicate technical concepts to non-technical stakeholders.

Be ready to go over:

• Requirement Gathering – Translating a vague business request into a concrete data engineering task.
• Stakeholder Management – Handling shifting priorities and managing expectations regarding data delivery timelines.
• Productionizing ML Models – Collaborating with Data Scientists to take a model from a Jupyter notebook to a scalable data pipeline.
• Advanced concepts (less common) – Leading incident post-mortems and driving data governance initiatives across departments.

Example questions or scenarios:

• "Tell me about a time you had to push back on a stakeholder who requested real-time data when batch processing was more appropriate."
• "Describe a project where you collaborated with a Data Scientist. What was your role, and how did you ensure the data pipeline met their needs?"
• "How do you handle situations where a downstream user reports that the data in their dashboard looks 'wrong'?"

Key Responsibilities

As a Data Engineer at Aircall, your day-to-day work revolves around building and maintaining the infrastructure that powers the company's data ecosystem. You will be responsible for developing scalable ETL/ELT pipelines that ingest high-velocity telephony data, CRM sync logs, and internal application metrics into the central cloud data warehouse. This requires writing clean, testable code and ensuring that orchestration frameworks are running smoothly and efficiently.

Collaboration is a massive part of this role. You will work side-by-side with Data Scientists to provision the clean, structured datasets they need to train models for features like call transcription and sentiment analysis. You will also partner with Product Managers and Business Intelligence teams to understand their reporting needs, translating complex business logic into reliable data models and aggregated tables using tools like dbt.

Beyond building new features, you will take ownership of data reliability and platform health. This includes implementing rigorous data quality checks, monitoring pipeline performance, optimizing expensive queries to manage cloud computing costs, and responding to data-related incidents. You will act as a steward of data governance, ensuring that Aircall's data remains secure, compliant, and highly available for all downstream consumers.

Role Requirements & Qualifications

A strong candidate for the Data Engineer position at Aircall brings a mix of deep technical expertise and a product-oriented mindset. The Data and Science team looks for individuals who have proven experience operating at scale and who understand the nuances of modern data stacks.

Technical Skills:

• Must-have skills – Expert-level SQL and strong proficiency in Python. Deep experience with cloud data warehouses (e.g., Snowflake, BigQuery, or Redshift). Hands-on experience with orchestration tools like Apache Airflow. Solid understanding of dimensional data modeling and ETL/ELT paradigms.
• Nice-to-have skills – Experience with dbt for data transformation. Familiarity with streaming technologies (Kafka, Kinesis) and event-driven architectures. Previous exposure to telephony, voice data, or complex SaaS CRM integrations.

Experience Level:

• Typically requires 4+ years of dedicated Data Engineering experience.
• Proven track record of designing and deploying production-grade data pipelines.
• Experience working in fast-paced, high-growth SaaS or cloud-native environments.

Soft Skills:

• Excellent communication skills to bridge the gap between technical infrastructure and business strategy.
• High autonomy and a proactive approach to identifying data quality issues before they impact end-users.
• Strong collaborative mindset, particularly when working alongside Data Science and Analytics peers.

Common Interview Questions

The questions below represent the types of challenges you will face during the Aircall interview process. They are drawn from patterns observed in similar data engineering interviews and are intended to help you understand the format and rigor of the evaluation, rather than serving as a strict memorization list.

SQL and Data Manipulation

These questions test your ability to extract, aggregate, and transform data using advanced SQL features.

• Write a query to find the number of overlapping calls handled by a specific agent on a given day.
• How would you write a query to identify the top 10% of customers by call volume, partitioned by region?
• Calculate the month-over-month growth rate of active Aircall users using window functions.
• Given a table of call events with timestamps, write a query to find the session duration for each user.
• Explain the difference between RANK(), DENSE_RANK(), and ROW_NUMBER() with a practical example.

Python and Coding

These questions assess your algorithmic thinking, data structure knowledge, and ability to interact with APIs.

• Write a Python script to fetch paginated data from a REST API, handle rate-limit errors, and save the output to a JSON file.
• Given a list of dictionaries representing call logs, write a function to group and sum the call durations by customer ID.
• How would you merge two large, unsorted CSV files containing user data in Python without running out of memory?
• Write a function to detect anomalies in a time-series array of daily call volumes.
• Explain how you would write unit tests for a Python data transformation function.

System Design and Architecture

These questions evaluate your high-level thinking regarding scalability, fault tolerance, and data modeling.

• Design a real-time dashboard architecture that displays active calls and agent availability.
• Walk me through how you would design a data warehouse schema to support both billing analytics and product usage metrics.
• We need to ingest and process 10 million call transcription events per day. Describe the end-to-end pipeline you would build.
• How do you handle schema evolution in a production database without disrupting downstream pipelines?
• Compare the trade-offs between an ETL and an ELT approach for our specific use case.

Behavioral and Impact

These questions focus on your past experiences, problem-solving methodology, and cultural alignment.

• Tell me about a time you designed a pipeline that failed in production. What happened, and how did you fix it?
• Describe a situation where you had to balance technical debt with the need to deliver a feature quickly.
• How do you ensure that the data you are providing to the business is accurate and trustworthy?
• Tell me about a time you had to explain a complex technical data issue to a non-technical stakeholder.
• Give an example of how you proactively identified a bottleneck in your data infrastructure and resolved it.

Frequently Asked Questions

Q: How difficult is the technical screen compared to standard LeetCode interviews? The technical screen for this role leans more toward practical, applied data engineering rather than abstract algorithmic puzzles. You will likely face SQL questions involving window functions and joins, and Python questions focused on data manipulation or API handling. Preparing with medium-level SQL and data-centric Python challenges is highly recommended.

Q: What differentiates a successful candidate in the Aircall process? Successful candidates do not just write code; they demonstrate a deep understanding of the business context. They ask clarifying questions about data volume, downstream consumers, and business goals before designing a pipeline or writing a query. Showing that you care about data quality and cost optimization sets you apart.

Q: What is the working style like on the Data and Science team? The team operates highly collaboratively, often acting as a central nervous system for the company. You will experience a mix of deep-focus engineering work and cross-functional meetings. The culture heavily promotes transparency, peer reviews, and continuous learning, especially given the rapid evolution of modern data tools.

Q: What is the typical timeline from the initial screen to an offer? The process typically takes between 3 to 5 weeks from the recruiter screen to the final decision. Aircall values candidate experience and generally moves quickly between stages, providing timely feedback after the technical screen and the onsite loop.

Q: Is this role fully remote or hybrid? This specific Data Engineer role is tied to the Seattle, WA location. You should expect a hybrid working model, requiring you to be in the office for a certain number of days a week to foster team collaboration and whiteboarding sessions. Clarify the exact days-in-office policy with your recruiter during the initial call.

Other General Tips

• Think Out Loud During Technical Rounds: Interviewers at Aircall care as much about your problem-solving process as they do about the final answer. Narrate your thought process, explain why you are choosing a specific data structure, and discuss potential edge cases before you start typing.

• Focus on Business Value: When answering behavioral or design questions, always tie your technical decisions back to business outcomes. Explain how your pipeline improved reporting speed, saved cloud costs, or enabled a new product feature.

• Know the Aircall Product: Take time to understand Aircall's core offering. Think about the types of data a cloud telephony system generates (call duration, queue times, CRM sync statuses, voice transcriptions) and anticipate how you would model and process that specific data.

• Admit What You Don't Know: If you are asked about a specific tool (e.g., Kafka) and your experience is primarily with another (e.g., Kinesis), be honest. Explain your conceptual understanding and highlight your ability to learn new technologies quickly.

• Prepare Questions for Your Interviewers: The end of the interview is your chance to evaluate Aircall. Ask insightful questions about their current data stack, their biggest infrastructure bottlenecks, or how the Data Engineering team interacts with Product. This shows genuine interest and strategic thinking.

Summary & Next Steps

Joining Aircall as a Data Engineer offers a unique opportunity to build high-impact infrastructure at the intersection of cloud telephony and advanced analytics. You will be tackling complex challenges related to scale, real-time processing, and data quality, all while directly enabling Data Scientists and business leaders to drive the company forward. The work is challenging, but it is deeply rewarding for engineers who love seeing their pipelines power tangible business intelligence.

The salary data above provides a clear view of the compensation expectations for this role in the Seattle market. Use this information to confidently navigate the offer stage, keeping in mind that total compensation may also include equity, bonuses, and comprehensive benefits depending on your specific experience level.

As you move forward, focus your preparation on mastering advanced SQL, refining your pipeline design narratives, and practicing clear communication. Remember that the interviewers want you to succeed—they are looking for a capable teammate, not trying to trick you. Approach each round with confidence, lean on your practical experience, and utilize resources like Dataford to refine your technical edge. You have the skills to excel in this process; now it is just about demonstrating your impact.