What is a Data Engineer at Cohere?

As a Data Engineer at Cohere, you are at the core of our mission to transform healthcare through clinical intelligence and operational excellence. You will join our Data Platform team to design, build, and scale the critical infrastructure that powers analytics, operations, and product features across the organization. This is not just about moving data from point A to point B; it is about building high-trust, governed, and reliable data products that directly impact patient care and business outcomes.

You will operate across the entire data lifecycle, from ingestion to transformation and integration. Because our platform handles complex, high-volume healthcare data, your work will heavily influence platform-wide design decisions, technical standards, and schema governance. You will partner closely with analytics engineers, architects, product leaders, and compliance teams to ensure our data ecosystem remains performant, scalable, and secure.

Whether you are joining as a Senior or Staff Engineer, you are expected to be a force multiplier. This means you will not only write clean, maintainable code in Python and SQL, but you will also mentor junior engineers, drive cross-squad initiatives like observability frameworks, and evaluate emerging technologies to continuously reduce our total cost of ownership. Expect a highly collaborative, fast-paced environment where your architectural decisions will shape the future of Cohere's data capabilities.

Common Interview Questions

Our interview questions are designed to test both your theoretical knowledge and your practical experience. While the specific questions will vary based on your interviewer and the flow of the conversation, the following examples represent the types of challenges you should be prepared to discuss.

Data Architecture & System Design

• Design a scalable data platform for a healthcare application that requires both real-time operational reporting and batch analytical processing.
• How would you evaluate and choose between using Athena, EMR, or a traditional data warehouse for a new analytics initiative?
• Walk us through your strategy for migrating an existing data lake to an Iceberg-based architecture. What are the risks, and how do you mitigate them?
• Explain how you would design a system to handle late-arriving data in a daily batch pipeline.

Data Modeling & Pipeline Engineering

• Describe a complex data pipeline you built from scratch. What were the most significant technical hurdles, and how did you overcome them?
• How do you approach designing idempotent pipelines in Airflow? Give an example of a time this saved you during a production failure.
• Write a SQL query using window functions to identify the top 3 most expensive medical claims per patient over a rolling 12-month period.
• How do you use dbt to manage complex dependencies and ensure data quality in your transformation layer?

Operational Rigor & Observability

• What metrics and alerts do you put in place to ensure a critical data pipeline is healthy?
• Tell us about a time a data pipeline failed silently in production. How did you discover it, fix it, and prevent it from happening again?
• How do you implement and enforce data contracts between software engineering teams and the data platform team?

Leadership & Collaboration

• Tell me about a time you had to push back on a product requirement because it compromised the scalability or governance of the data platform.
• How do you approach mentoring junior engineers and elevating the overall engineering standards of your team?
• Describe a cross-functional initiative you led. How did you align stakeholders from analytics, engineering, and product?

Getting Ready for Your Interviews

Preparation is key to succeeding in our interview process. We evaluate candidates holistically, looking for a blend of deep technical expertise, strategic thinking, and the ability to influence cross-functional teams.

Architectural & Systems Design – We assess your ability to design scalable, reliable data platforms. Interviewers will look at how you balance immediate business needs with long-term technical roadmaps, your familiarity with modern data stack tools, and how you handle trade-offs in storage formats and processing frameworks.

Data Engineering Craft – This covers your hands-on ability to write clean, testable, and maintainable code, primarily in Python and SQL. We evaluate your mastery of data modeling, pipeline optimization, and your experience with orchestration tools and distributed processing.

Operational Excellence – We need engineers who champion engineering rigor. You will be evaluated on your approach to observability, data quality, schema validation, CI/CD practices, and incident prevention.

Leadership & Collaboration – Especially for Senior and Staff roles, we look for your ability to mentor others, drive cross-squad initiatives, and partner effectively with non-technical stakeholders. We want to see how you navigate ambiguity and communicate complex technical concepts to product and business leaders.

Interview Process Overview

The interview process for Data Engineers at Cohere is designed to be rigorous but conversational. We want to understand how you think, how you build, and how you collaborate. You will typically start with a recruiter screen to align on your background, career goals, and role expectations. This is followed by a technical screen with a hiring manager or senior engineer, which usually involves a mix of high-level architecture discussion and a practical coding or SQL assessment.

If you move forward to the virtual onsite stage, expect a comprehensive series of interviews. These rounds will dive deeply into system design, data modeling, pipeline architecture, and behavioral competencies. We emphasize real-world scenarios over algorithmic puzzles. You will be asked to design systems similar to what we build at Cohere, discuss past projects where you drove technical vision, and explain how you handle operational challenges like data quality failures or pipeline bottlenecks.

Throughout the process, our interviewers are looking for a collaborative mindset. We value candidates who ask clarifying questions, communicate their assumptions clearly, and are receptive to feedback.

This visual timeline outlines the typical stages of our interview loop, from the initial recruiter screen to the final virtual onsite rounds. Use this to structure your preparation, focusing first on core coding and SQL fundamentals before transitioning into deep-dive system design and behavioral storytelling. Keep in mind that specific rounds may be tailored slightly depending on whether you are interviewing for a Senior or Staff level position.

Deep Dive into Evaluation Areas

To excel in your interviews, you need to demonstrate mastery across several core domains. Our interviewers will probe your depth of knowledge and your practical experience in building resilient data platforms.

Data Architecture and System Design

System design is a critical component of our evaluation, particularly for Senior and Staff roles. We want to see how you piece together various technologies to build scalable, fault-tolerant data pipelines. You should be prepared to discuss batch versus streaming architectures, data lakehouse concepts, and storage optimization. Strong performance here means you can confidently justify your technology choices, discuss bottlenecks, and design for scale and cost-efficiency.

Be ready to go over:

• Distributed Processing – Frameworks like EMR or Spark, and how to optimize large-scale data transformations.
• Modern Table Formats – The benefits and mechanics of Iceberg or Parquet for efficient data storage and retrieval.
• Streaming & Messaging – Using Kafka for real-time data ingestion and event-driven architectures.
• Advanced concepts – Data mesh architectures, decoupling compute from storage (e.g., Athena), and designing for multi-region high availability.

Example questions or scenarios:

• "Design a real-time data ingestion pipeline using Kafka that eventually lands in an Iceberg table for analytical querying."
• "How would you architect a solution to migrate legacy batch jobs to a more scalable, cost-effective infrastructure using AWS EMR and Airflow?"
• "Walk me through the trade-offs between using a traditional data warehouse versus a data lakehouse architecture for clinical intelligence reporting."

Data Modeling and Governance

At Cohere, trustworthy data is non-negotiable. We evaluate your ability to design robust data models and enforce strict governance practices. You should understand how to translate complex business requirements into logical and physical data models. A strong candidate will emphasize schema evolution, data contracts, and automated quality checks.

Be ready to go over:

• Analytical Modeling – Dimensional modeling, snowflake/star schemas, and using tools like dbt for transformations.
• Data Quality & Observability – Implementing automated tests, anomaly detection, and data contract enforcement.
• Schema Validation – Managing schema evolution safely in production environments.
• Advanced concepts – Master data management in healthcare, handling personally identifiable information (PII), and compliance-driven data masking.

Example questions or scenarios:

• "How do you enforce data quality and schema validation in a pipeline that ingests data from multiple third-party vendors?"
• "Explain your approach to designing a data model for a new analytics dashboard. How do you ensure the model is both performant and easily extensible?"
• "Describe a time you implemented data contracts across different engineering squads. What were the challenges and outcomes?"

Pipeline Engineering and Coding Craft

Your hands-on coding skills are essential. We evaluate your proficiency in Python and SQL, focusing on your ability to write clean, modular, and maintainable code. Interviewers will look for your understanding of software engineering best practices applied to data engineering, including version control, testing, and CI/CD.

Be ready to go over:

• Python for Data Engineering – Writing robust ingestion scripts, interacting with APIs, and handling exceptions gracefully.
• Advanced SQL – Complex window functions, performance tuning, and query optimization in distributed environments like Athena.
• Orchestration – Designing modular and idempotent DAGs in Airflow.
• Advanced concepts – Building custom Airflow operators, optimizing Spark configurations, and implementing automated testing for data pipelines.

Example questions or scenarios:

• "Write a Python script to ingest paginated data from a REST API, handle rate limits, and load the data into an S3 bucket."
• "Given a complex SQL query that is timing out in production, walk me through your steps to identify the bottleneck and optimize it."
• "How do you design Airflow DAGs to ensure they are fully idempotent and can easily recover from mid-execution failures?"

Key Responsibilities

As a Data Engineer at Cohere, your day-to-day work revolves around building and maintaining the backbone of our data ecosystem. You will take ownership of designing and delivering large-scale data pipelines that power everything from internal analytics to operational clinical intelligence. This involves writing production-grade Python and SQL, orchestrating workflows with Airflow, and optimizing storage using Iceberg and Parquet on AWS.

Collaboration is a massive part of this role. You will partner closely with analytics engineers to ensure data products are performant and trustworthy, and work with product stakeholders to align technical solutions with business needs. You will also lead cross-squad initiatives, such as establishing platform-wide observability frameworks, enforcing data contracts, and improving schema governance.

Beyond writing code, you will serve as a technical mentor and design authority. You will evaluate emerging technologies to enhance developer experience and reduce costs, champion engineering rigor through thorough documentation and code reviews, and participate in on-call rotations to ensure the reliability of critical platform jobs. Your strategic thinking will directly shape the long-term technical roadmap of the Data Platform.

Role Requirements & Qualifications

To thrive as a Data Engineer at Cohere, you need a strong foundation in modern data engineering practices and a proven track record of delivering scalable solutions in cloud environments.

• Must-have technical skills – Deep expertise in Python and SQL. Extensive experience with workflow orchestration (Airflow), data transformation (dbt), and AWS data services (Athena, EMR). Proficiency with modern storage formats like Iceberg and Parquet.
• Must-have experience – 5+ years of experience in data engineering or software development, with a strong focus on data platforms. Experience operating across the end-to-end data lifecycle, from ingestion to integration.
• Must-have soft skills – Strong communication skills to partner with cross-functional stakeholders (analytics, product, compliance). A track record of mentoring junior engineers and championing a culture of engineering excellence.
• Nice-to-have skills – Experience with real-time streaming technologies (Kafka). Background in healthcare data, clinical intelligence, or handling sensitive compliance requirements (HIPAA). Experience driving platform-wide architectural strategy (highly preferred for Staff level).

Frequently Asked Questions

Q: How technical are the interviews compared to standard software engineering roles? Our interviews focus heavily on data-specific engineering. While you must write clean, efficient code (primarily Python and SQL), we care less about obscure algorithmic puzzles and more about your ability to build robust pipelines, design scalable systems, and apply software engineering best practices (like CI/CD and testing) to data infrastructure.

Q: What differentiates a strong candidate from an average one? Strong candidates do more than just use tools; they understand the underlying mechanics and trade-offs. They can explain why they chose Iceberg over Delta Lake, or how they optimized an Airflow DAG for cost-efficiency. They also demonstrate a strong focus on business impact, observability, and data governance.

Q: Is healthcare domain experience required? While experience with healthcare data (and compliance standards like HIPAA) is a strong plus, it is not strictly required. We value strong foundational data engineering skills and a willingness to learn the complexities of clinical data over prior domain expertise.

Q: How long does the interview process typically take? The process usually takes 2 to 4 weeks from the initial recruiter screen to the final decision. We strive to move quickly and provide prompt feedback after the virtual onsite rounds.

Q: What is the remote work culture like for this team? This is a remote-friendly role. We rely heavily on asynchronous communication, thorough documentation, and clear technical standards to collaborate effectively across different time zones. You must be comfortable driving initiatives independently in a remote environment.

Other General Tips

• Focus on Trade-offs: In system design discussions, there is rarely one perfect answer. Call out the pros and cons of your architecture choices, especially regarding cost, maintainability, and scalability.
• Think Out Loud: During technical screens, verbalize your thought process. If you are stuck on a SQL query or Python script, explaining your logic helps the interviewer guide you and assesses your problem-solving approach.

• Highlight Operational Rigor: Do not just talk about the "happy path." Explain how your code handles failures, retries, bad data, and alerts. We highly value engineers who build for operational resilience.
• Structure Your Behavioral Answers: Use the STAR method (Situation, Task, Action, Result) for leadership and collaboration questions. Be specific about your individual contributions, especially when discussing cross-squad initiatives.

• Ask Insightful Questions: Use the time at the end of the interview to ask about our data stack, our biggest platform challenges, or how we handle data governance. This shows genuine interest and helps you evaluate if Cohere is the right fit for you.

Summary & Next Steps

Joining Cohere as a Data Engineer offers a unique opportunity to build scalable, high-impact data infrastructure that directly supports clinical intelligence and healthcare operations. You will be tackling complex challenges at scale, driving architectural strategy, and elevating the engineering standards of the entire Data Platform team.

This module provides an overview of the expected base salary range for this position. Keep in mind that total compensation may also include equity, bonuses, and comprehensive benefits, which will be discussed in detail by your recruiter based on your experience level and location.

To succeed in your interviews, focus on demonstrating a strong balance of hands-on coding craft, deep system design knowledge, and a rigorous approach to data quality and observability. Review your past projects, practice articulating your architectural decisions, and be ready to showcase your ability to mentor and lead cross-functional initiatives. For more detailed insights, practice questions, and peer experiences, be sure to explore the resources available on Dataford. You have the skills to make a massive impact here—prepare thoroughly, stay confident, and show us how you build for the future!