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

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?"