Interview Guides

Cognite Data Engineer Interview Questions & Guide 2026

CogniteData Engineer

Updated Jun 9, 2026

Cognite Data Engineer interview questions & guide 2026

Every question Cognite interviewers actually ask, the frameworks that win the room, and the language hiring managers respond to.

Question bank

What is a Data Engineer at Cognite?

As a Data Engineer at Cognite, you will play a critical role in realizing the company’s core mission: liberating industrial data to power sustainable, data-driven decisions. Cognite is a global leader in industrial software-as-a-service (SaaS), and its flagship product, Cognite Data Fusion (CDF), acts as a central engine that ingests, cleans, and contextualizes massive streams of operational technology (OT), information technology (IT), and visual data. Your work will directly impact how heavy industries—such as energy, manufacturing, and shipping—operationalize their data to reduce carbon footprints, optimize asset performance, and scale artificial intelligence models.

This role is highly technical and demands a deep understanding of data modeling, distributed computing, and pipeline orchestration. You will design, build, and maintain robust data pipelines that process massive volumes of time-series, geospatial, and structured relational data. Unlike traditional software environments, engineering at Cognite requires you to solve complex data integration challenges where data sources are often siloed, legacy-heavy, and highly fragmented.

You will work alongside highly collaborative teams of Data Scientists, Product Managers, and Solution Architects. Together, you will build scalable data architectures that transform raw industrial telemetry into structured, queryable assets. If you are passionate about solving real-world, high-scale engineering problems that bridge the physical and digital worlds, this position offers an unmatched opportunity for professional growth and impact.

Common Interview Questions

The following questions are representative of what you can expect during the Cognite hiring process. These questions are drawn from real candidate experiences and are grouped into key functional categories to help you identify patterns and structure your preparation.

Python & Algorithmic Coding

These questions assess your core programming skills, code readability, and your ability to write clean, efficient Python code under timed conditions.

Write a Python function to find the first non-repeating character in a stream of sensor data.
Implement a basic rate-limiter in Python to throttle incoming API requests from an industrial IoT gateway.

Given a list of time-series data points with missing values, write a script to perform forward-fill and backward-fill operations.
Explain the difference between generators and lists in Python, and write a generator function that processes a large log file line-by-line.
Solve a classic string manipulation or array-based algorithmic problem using optimal time and space complexity.

SQL & Data Warehousing

These questions evaluate your ability to query complex datasets, model relational databases, and optimize query performance for large-scale analytical workloads.

Write a window function in SQL to calculate the rolling average of machine temperatures over a 10-minute moving window.
How would you design a database schema to support both real-time asset tracking and historical analysis of heavy machinery?
Explain the difference between inner, outer, left, and right joins, and describe a scenario where using an anti-join is the most efficient approach.
How do you optimize a slow-running SQL query that involves joining a massive time-series table with a small asset metadata table?
Compare and contrast relational databases (OLTP) and columnar data warehouses (OLAP) in the context of industrial data storage.

System Design & Industrial Use Cases

These questions focus on your ability to architect end-to-end data systems, handle scale, and design pipelines tailored to industrial IoT and cloud infrastructures.

Design a data ingestion pipeline that can handle millions of concurrent events per second from wind turbine sensors.
How would you design a data validation and cleaning framework using Databricks or Apache Spark to handle corrupt or out-of-order data?
Explain how you would structure an ETL pipeline to move data from an on-premise industrial historian (like OSIsoft PI) to a cloud data lake.
What strategies would you employ to handle schema evolution in a data pipeline where source systems frequently add or modify fields?
How do you ensure data quality, monitoring, and alerting across a multi-stage data processing pipeline?

Behavioral & Cultural Fit

These questions assess your working style, problem-solving mindset, and alignment with the collaborative culture at Cognite.

Tell me about a time when you had to deal with an ambiguous data requirement. How did you define the scope and deliver the project?
Why do you want to work at Cognite, and how does your background align with industrial data engineering?
Describe a situation where you had a disagreement with a team member or stakeholder regarding a technical decision. How did you resolve it?
How do you manage your time and prioritize tasks when working on multiple high-priority engineering deliverables?

Be ready to go over:

Data structures – Deep understanding of lists, dictionaries, sets, tuples, and when to use each for optimal performance.
Time and space complexity – Ability to analyze and optimize your code using Big O notation.
Clean code principles – Writing readable code with meaningful variable names, proper comments, and structured error handling.
Advanced concepts (less common) – Multithreading vs. multiprocessing in Python, custom decorators, and memory-efficient generator patterns.

Example scenarios:

"Write a script to parse a directory of JSON log files, extract specific sensor metrics, and aggregate them by hour."
"Implement an algorithm to detect anomalous spikes in a stream of temperature readings based on a sliding window standard deviation."

SQL & Data Architecture

This area focuses on your ability to manipulate large datasets and design robust relational schemas. You must demonstrate that you can write complex queries and understand the underlying mechanics of database engines.

Be ready to go over:

Analytical queries – Mastery of window functions, common table expressions (CTEs), and complex aggregations.
Schema design – Designing star schemas, snowflake schemas, and normalizing/denormalizing tables based on access patterns.
Query optimization – Understanding execution plans, indexing strategies, and partitioning.
Advanced concepts (less common) – Query profiling, database locking mechanisms, and managing stateful stream processing joins.

Example scenarios:

"Write a query to identify the top 3 most active industrial assets per region for each month over the past year."
"Design a schema to store high-frequency time-series data from offshore drilling rigs, optimizing for fast write speeds and low storage footprint."

Industrial Data Pipelines & Case Studies

This evaluation area tests your ability to design end-to-end data systems that scale. You will be given a high-level industrial use case and asked to architect a complete pipeline solution.

Tip

You do not need a deep background in heavy industry, but showing curiosity about how physical assets (like pumps, wind turbines, or offshore platforms) translate into digital data points will set you apart.

Be ready to go over:

Ingestion strategies – Handling batch vs. real-time streaming data, backpressure, and data buffering.
Processing frameworks – Leveraging tools like Databricks, Apache Spark, or cloud-native ETL services.
Data quality and monitoring – Implementing schema validation, data lineage tracking, and automated alerting systems.
Advanced concepts (less common) – Integrating operational technology protocols (like MQTT or OPC-UA) with modern cloud data lakes.

Example scenarios:

"Walk me through how you would architect a pipeline to ingest, clean, and store 50,000 events per second of vibration data from factory floor sensors."
"How would you design a data reconciliation pipeline to handle delayed data arrivals from remote maritime vessels with intermittent internet connectivity?"

09 · Topic breakdown

What they actually test for

Topic distribution

All topics

PythonSQLProblem SolvingProgramming (Coding Tests)Databricks

Key Responsibilities

As a Data Engineer at Cognite, your day-to-day work will revolve around building the data foundation that powers industrial digital transformation.

You will spend a significant portion of your time designing, developing, and deploying robust data pipelines to ingest diverse datasets into Cognite Data Fusion (CDF). This involves writing highly optimized ETL/ELT jobs, integrating with various industrial source systems, and ensuring that data is cleaned, validated, and accurately contextualized. You will collaborate closely with customer-facing teams and internal software engineers to understand data integration requirements and translate them into scalable technical architectures.

Additionally, you will be responsible for maintaining the health, performance, and reliability of the data infrastructure. This includes monitoring pipeline executions, troubleshooting performance bottlenecks in Spark or cloud databases, and continuously refining data models to support evolving analytics use cases. You will also contribute to engineering best practices by writing comprehensive documentation, participating in code reviews, and mentoring junior engineers to maintain high standards of code quality and system reliability.

Role Requirements & Qualifications

To be competitive for the Data Engineer position at Cognite, you should possess a strong technical background and a proven track record of delivering scalable data solutions.

Technical Skills

Must-have skills – Strong proficiency in Python and SQL; hands-on experience with distributed data processing frameworks such as Apache Spark or Databricks; experience building ETL/ELT pipelines in major cloud environments (AWS, Azure, or GCP); familiarity with relational and non-relational database technologies.
Nice-to-have skills – Experience with workflow orchestration tools like Apache Airflow; familiarity with containerization (Docker, Kubernetes); exposure to industrial data formats, time-series databases, or IoT protocols (OPC-UA, MQTT).

Experience & Soft Skills

Experience level – Typically requires 3+ years of professional experience in a dedicated data engineering or software engineering role, with a focus on building data-intensive applications.
Soft skills – Strong analytical and problem-solving mindset; excellent communication skills with the ability to articulate technical trade-offs; adaptability in navigating fast-paced, ambiguous project requirements; collaborative spirit with a focus on team success.

Frequently Asked Questions

Q: How difficult is the technical assessment at Cognite? The technical assessment is generally considered to be of average difficulty, but it can feel challenging due to the rigidity of the coding platforms used. Success requires clean, structured coding and a strong foundation in Python algorithms and SQL optimizations.

Q: Do I need prior experience in heavy industries like oil & gas or manufacturing? No, prior industrial experience is not a strict requirement. However, you must demonstrate a strong interest in learning about industrial data challenges, physical assets, and how to apply modern software engineering practices to legacy industrial systems.

Q: What is the typical timeline for the hiring process? The timeline can vary. Some candidates complete the process within a few weeks, while others experience a longer process stretching over a couple of months, depending on team availability and scheduling.

Q: What is the culture like within the engineering teams at Cognite? The engineering culture is highly collaborative, innovative, and flat-structured. Teams value open technical discussions, continuous learning, and a strong sense of ownership over the products and systems they build.

Other General Tips

To maximize your chances of securing an offer, keep these practical, insider tips in mind as you navigate the interview process.

Master the fundamentals of Spark and Databricks: Cognite heavily leverages modern cloud data stacks. Be prepared to discuss how you optimize Spark jobs, manage cluster configurations, and handle large-scale data transformations efficiently.
Practice structured behavioral communication: When answering behavioral questions, keep your responses concise, objective, and professional.

Tip

During behavioral rounds, focus on delivering structured answers using the STAR method (Situation, Task, Action, Result) to keep your answers objective and professional.

Showcase architectural trade-offs: During system design rounds, there is rarely a single "correct" answer. Always present multiple approaches, discuss their pros and cons (e.g., cost, latency, complexity), and justify why you chose a specific architecture.
Clarify early and often: In coding and system design interviews, do not start writing code or drawing architectures immediately. Ask clarifying questions to define input/output formats, edge cases, and scale requirements first.

Summary & Next Steps

The Data Engineer role at Cognite is an exceptional opportunity to work on highly complex, large-scale data challenges that have a direct, positive impact on industrial efficiency and global sustainability. By helping heavy industries unlock the value of their operational data, you will be at the forefront of the next industrial revolution.

To succeed in this interview process, focus your preparation on mastering Python coding fundamentals, writing optimized SQL queries, and designing scalable system architectures. Remember to communicate clearly, structure your thoughts methodically, and show curiosity about Cognite's unique industrial domain.

To further accelerate your preparation and gain deeper insights into interview patterns, compensation structures, and real candidate feedback, explore the comprehensive resources available on Dataford. With focused preparation and the right strategic approach, you can confidently showcase your expertise and secure your place on the team.

The salary data displayed above represents the estimated total compensation package for a Data Engineer at Cognite. When evaluating this compensation structure, keep in mind that total packages typically consist of a competitive base salary, performance-based bonuses, and equity or stock options. You should use these insights to guide your expectations and inform your discussions during the offer negotiation phase.

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Interview Guides

Cognite Data Engineer interview questions & guide 2026

What is a Data Engineer at Cognite?

Common Interview Questions

Python & Algorithmic Coding

SQL & Data Warehousing

System Design & Industrial Use Cases

Behavioral & Cultural Fit

The questions most likely to come up

See how a strong candidate would approach this

Design Data Quality Controls Pipeline

Getting Ready for Your Interviews

Interview Process Overview

The interview process, end to end

Note

Deep Dive into Evaluation Areas

Python & Algorithmic Problem Solving

Note

SQL & Data Architecture

Industrial Data Pipelines & Case Studies

Tip

What they actually test for

Key Responsibilities

Role Requirements & Qualifications

Technical Skills

Experience & Soft Skills

Frequently Asked Questions

Other General Tips

Tip

Summary & Next Steps