Cohesity Data Engineer Interview Guide

Getting ready for a Data Engineer interview at Cohesity? The Cohesity Data Engineer interview process typically spans a range of question topics and evaluates skills in areas like data pipeline design, ETL processes, large-scale data architecture, and communicating technical insights to diverse audiences. Interview prep is especially important for this role at Cohesity, as Data Engineers are expected to build robust, scalable data infrastructure and ensure data integrity across complex, high-volume systems that directly support the company’s focus on data management, protection, and accessibility.

In preparing for the interview, you should:

• Understand the core skills necessary for Data Engineer positions at Cohesity.
• Gain insights into Cohesity’s Data Engineer interview structure and process.
• Practice real Cohesity Data Engineer interview questions to sharpen your performance.

At Interview Query, we regularly analyze interview experience data shared by candidates. This guide uses that data to provide an overview of the Cohesity Data Engineer interview process, along with sample questions and preparation tips tailored to help you succeed.

Cohesity is a leading provider of data management solutions, specializing in simplifying the way organizations protect, manage, and derive value from their data across hybrid and multicloud environments. The company’s platform consolidates backup, recovery, file and object services, and analytics, helping enterprises reduce complexity and increase security. Cohesity serves a global customer base, including Fortune 500 companies, and is recognized for its innovation in combating ransomware and ensuring data resilience. As a Data Engineer, you will contribute to building robust data infrastructure that supports Cohesity’s mission to empower organizations with secure, efficient, and actionable data management.

As a Data Engineer at Cohesity, you are responsible for designing, building, and maintaining scalable data pipelines and infrastructure that support the company’s enterprise data management solutions. You will work closely with software engineers, product managers, and data scientists to ensure efficient data ingestion, transformation, and storage processes. Key tasks include optimizing data workflows, implementing best practices for data reliability and security, and supporting analytics initiatives that drive product innovation and customer success. This role is essential in enabling Cohesity to deliver robust, high-performance data services for its clients, contributing directly to the company’s mission of simplifying data management at scale.

2.1 Stage 1: Application & Resume Review

The process begins with a detailed screening of your application materials, with a focus on your experience in designing and maintaining scalable data pipelines, ETL workflows, and data warehouse solutions. Emphasis is placed on your proficiency with SQL, Python, and distributed data systems, as well as your track record in handling large-scale data ingestion, cleaning, transformation, and analytics. Highlighting relevant projects, particularly those involving real-time streaming, data quality initiatives, and cross-functional collaboration, will strengthen your candidacy at this stage.

2.2 Stage 2: Recruiter Screen

A recruiter will reach out for a 20–30 minute conversation to discuss your background, motivation for applying to Cohesity, and alignment with the company’s mission and values. Expect questions about your career trajectory, interest in data engineering, and familiarity with Cohesity’s technology stack. This is also an opportunity to demonstrate strong communication skills and your ability to explain technical concepts in accessible terms. Preparation should include concise stories about your impact in previous roles and clear articulation of why you want to join Cohesity.

2.3 Stage 3: Technical/Case/Skills Round

This stage typically involves one or more interviews led by data engineering team members or a technical lead, focusing on your hands-on skills. You can expect live coding exercises assessing your expertise in SQL (such as writing complex queries, aggregation, and data manipulation), Python (including data parsing, pipeline automation, and data structure manipulation), and system design (such as architecting robust, scalable ETL pipelines or data warehouses). Case-based discussions may cover real-world scenarios like designing ingestion pipelines for heterogeneous data sources, troubleshooting pipeline failures, optimizing data quality, or transitioning from batch to real-time streaming. To prepare, review your experience with big data tools, pipeline orchestration, and data modeling, and be ready to explain your design and debugging process.

2.4 Stage 4: Behavioral Interview

A behavioral interview is typically conducted by a hiring manager or senior engineer, emphasizing your problem-solving approach, adaptability, and teamwork. You’ll be asked to discuss past experiences overcoming hurdles in data projects, collaborating with cross-functional teams, and communicating complex insights to non-technical stakeholders. Scenarios may probe how you’ve handled ambiguity, prioritized tasks, or ensured data accessibility and quality. Prepare by reflecting on examples that showcase your leadership, resilience, and ability to make data-driven decisions under pressure.

2.5 Stage 5: Final/Onsite Round

The onsite (or virtual onsite) round usually consists of multiple back-to-back interviews with data engineers, analytics managers, and sometimes cross-functional partners from product or business teams. These interviews blend technical deep-dives—such as system design for data infrastructure, data quality assurance, and pipeline scalability—with advanced behavioral questions and case studies. You may also be asked to present a previous project, walk through your approach to a complex data challenge, or discuss trade-offs in technology choices. Demonstrating clear, structured thinking and the ability to communicate technical solutions to diverse audiences is key.

2.6 Stage 6: Offer & Negotiation

After successful completion of all interview rounds, the recruiter will present a formal offer. This stage covers compensation, benefits, start date, and any remaining questions about the role or company culture. Be prepared to discuss your expectations and to negotiate based on your experience and the value you bring to the team.

2.7 Average Timeline

The typical Cohesity Data Engineer interview process spans 3–4 weeks from application to offer, with each round generally taking place one week apart. Candidates with highly relevant experience or referrals may experience a faster progression, sometimes completing the process in as little as two weeks. Scheduling for onsite rounds and technical interviews may extend the timeline depending on team availability and candidate flexibility.

Next, let’s explore the specific types of interview questions you’re likely to encounter throughout the Cohesity Data Engineer process.

3.1 Data Pipeline Design and Scalability

Expect questions about designing robust, scalable data pipelines and transforming large datasets. Focus on demonstrating your ability to handle high-volume data, optimize ETL workflows, and ensure reliability across distributed systems.

3.1.1 Design a robust, scalable pipeline for uploading, parsing, storing, and reporting on customer CSV data
Outline each stage from ingestion to reporting, emphasizing error handling, schema validation, and scalability. Discuss automation, monitoring, and how you’d handle malformed files or spikes in data volume.
Example answer: “I’d use a cloud-based solution with parallel ingestion, schema enforcement at parsing, and incremental load to a warehouse. Monitoring and alerting would flag errors, and automated retries would handle transient failures.”

3.1.2 Redesign batch ingestion to real-time streaming for financial transactions
Compare batch and streaming architectures, highlighting latency, throughput, and fault tolerance. Recommend technologies (e.g., Kafka, Spark Streaming) and describe how you’d ensure data consistency and reliability.
Example answer: “I’d shift to a Kafka-based streaming ingestion, with Spark Streaming processing and real-time validation. This reduces latency and allows immediate fraud detection, while checkpoints ensure reliability.”

3.1.3 Design a scalable ETL pipeline for ingesting heterogeneous data from Skyscanner's partners
Explain how you’d handle schema variability, data quality, and partner-specific formats. Discuss modular pipeline design, error logging, and strategies for schema evolution.
Example answer: “I’d build a modular ETL with schema mapping layers, automated data profiling, and partner-specific connectors. Versioned schemas and logging would ensure smooth onboarding and troubleshooting.”

3.1.4 Design an end-to-end data pipeline to process and serve data for predicting bicycle rental volumes
Describe your approach from raw ingestion to model serving, including data cleaning, feature engineering, and serving predictions. Highlight how you’d automate retraining and monitor pipeline health.
Example answer: “I’d create an automated pipeline with scheduled ingestion, data cleaning, and feature extraction, followed by periodic model retraining and API-based prediction serving.”

3.1.5 Design a reporting pipeline for a major tech company using only open-source tools under strict budget constraints
Recommend open-source solutions for ETL, storage, and reporting. Discuss trade-offs, integration, and how you’d maintain reliability and scalability without commercial tools.
Example answer: “I’d use Airflow for orchestration, PostgreSQL for storage, and Metabase for reporting. Containerization and CI/CD pipelines would ensure maintainability and scalability.”

3.2 Data Modeling and Warehousing

These questions assess your ability to design logical and physical data models, optimize storage, and create efficient data warehouses. Focus on normalization, indexing, and supporting business analytics.

3.2.1 Design a data warehouse for a new online retailer
Explain your approach to schema design, fact and dimension tables, and supporting analytics. Discuss scalability and how you’d accommodate future growth.
Example answer: “I’d use a star schema with sales facts and dimensions for products, customers, and time. Partitioning and indexing would support fast queries, and incremental loading would handle growth.”

3.2.2 Write a SQL query to count transactions filtered by several criterias
Demonstrate filtering, aggregation, and optimizing queries for large datasets. Discuss indexing and query tuning for performance.
Example answer: “I’d use WHERE clauses for each filter and GROUP BY for aggregation, ensuring indexes on key columns to speed up query execution.”

3.2.3 Write a query to find all users that were at some point "Excited" and have never been "Bored" with a campaign
Show your ability to use conditional aggregation or subqueries to filter users based on event history.
Example answer: “I’d use a GROUP BY user with HAVING clauses to include those with ‘Excited’ events and exclude those with ‘Bored’ events.”

3.2.4 Modifying a billion rows
Discuss strategies for bulk updates, minimizing downtime, and optimizing for performance.
Example answer: “I’d use partitioning, batch updates, and possibly staging tables to update large volumes efficiently while minimizing locks.”

3.2.5 Design a data pipeline for hourly user analytics
Describe how you’d aggregate, store, and report hourly metrics, ensuring accuracy and scalability.
Example answer: “I’d use a streaming pipeline with windowed aggregations, store results in a time-series database, and schedule hourly reports.”

3.3 Data Quality, Cleaning, and Integration

Expect questions on ensuring data integrity, diagnosing pipeline failures, and integrating multiple data sources. Show your expertise in profiling, cleaning, and combining diverse datasets.

3.3.1 How would you systematically diagnose and resolve repeated failures in a nightly data transformation pipeline?
Describe your troubleshooting process, use of logs, automated alerts, and root cause analysis.
Example answer: “I’d review logs, set up automated failure alerts, and use dependency graphs to isolate bottlenecks. Root cause analysis would guide permanent fixes.”

3.3.2 Describing a real-world data cleaning and organization project
Share your approach to profiling, cleaning, and validating messy datasets, emphasizing reproducibility and documentation.
Example answer: “I’d profile for missing values, outliers, and duplicates, then apply targeted cleaning steps. Documentation and reproducible scripts would ensure auditability.”

3.3.3 You’re tasked with analyzing data from multiple sources, such as payment transactions, user behavior, and fraud detection logs. How would you approach solving a data analytics problem involving these diverse datasets? What steps would you take to clean, combine, and extract meaningful insights that could improve the system's performance?
Explain your process for schema alignment, data cleaning, and joining datasets to enable cross-source analytics.
Example answer: “I’d standardize schemas, clean for consistency, and use keys for joining. I’d profile merged data for completeness and extract actionable insights.”

3.3.4 Ensuring data quality within a complex ETL setup
Discuss your methods for validating data at each ETL stage, automated checks, and error handling.
Example answer: “I’d implement validation checks at each stage, automate anomaly detection, and use logging to track issues for rapid resolution.”

3.3.5 How would you approach improving the quality of airline data?
Describe strategies for profiling, cleaning, and setting up ongoing data quality monitoring.
Example answer: “I’d profile for missing and inconsistent values, automate cleaning scripts, and set up dashboards to monitor quality metrics over time.”

3.4 Data Engineering Tools and System Design

These questions evaluate your familiarity with data engineering tools, programming languages, and system design best practices. Highlight your experience with Python, SQL, cloud platforms, and trade-offs in technology choices.

3.4.1 python-vs-sql
Discuss scenarios where you’d prefer Python over SQL and vice versa, focusing on performance, scalability, and maintainability.
Example answer: “I use SQL for fast aggregations on structured data, and Python for complex transformations or integrating multiple sources.”

3.4.2 System design for a digital classroom service
Describe your approach to designing scalable, reliable systems for diverse user needs, including data storage and real-time analytics.
Example answer: “I’d use a microservices architecture with cloud-based storage, real-time analytics, and robust authentication for scalability and security.”

3.4.3 Designing a pipeline for ingesting media to built-in search within LinkedIn
Explain your pipeline design for ingesting, indexing, and serving search queries over large media datasets.
Example answer: “I’d use distributed ingestion with metadata extraction, store in a search-optimized index, and implement scalable search APIs.”

3.4.4 Design and describe key components of a RAG pipeline
Outline the architecture for Retrieval-Augmented Generation, focusing on document retrieval, embedding, and integration with LLMs.
Example answer: “I’d design a pipeline with vector search for retrieval, embedding generation, and orchestration between retrieval and generative models.”

3.4.5 Write a function that splits the data into two lists, one for training and one for testing
Explain your logic for random sampling, reproducibility, and ensuring balanced splits.
Example answer: “I’d shuffle the data, split by index, and ensure reproducibility with a fixed random seed.”

3.5 Behavioral Questions

3.5.1 Tell me about a time you used data to make a decision.
Describe a situation where your analysis directly influenced a business outcome. Focus on the decision-making process and measurable impact.

3.5.2 Describe a challenging data project and how you handled it.
Share a complex project, the hurdles faced, and the steps you took to overcome them. Emphasize resourcefulness and problem-solving.

3.5.3 How do you handle unclear requirements or ambiguity?
Explain your approach to clarifying objectives, communicating with stakeholders, and iterating on solutions.

3.5.4 Tell me about a time when your colleagues didn’t agree with your approach. What did you do to bring them into the conversation and address their concerns?
Discuss how you facilitated open dialogue, presented data-driven evidence, and reached consensus.

3.5.5 Describe a situation where two source systems reported different values for the same metric. How did you decide which one to trust?
Explain your process for investigating data lineage, validating sources, and communicating findings.

3.5.6 Give an example of automating recurrent data-quality checks so the same dirty-data crisis doesn’t happen again.
Share how you identified recurring issues, built automation, and improved data reliability.

3.5.7 Tell me about a time you delivered critical insights even though 30% of the dataset had nulls. What analytical trade-offs did you make?
Describe your approach to handling missing data, communicating uncertainty, and ensuring actionable results.

3.5.8 How do you prioritize multiple deadlines? Additionally, how do you stay organized when you have multiple deadlines?
Discuss your prioritization framework, time management strategies, and tools used for organization.

3.5.9 Share a story where you used data prototypes or wireframes to align stakeholders with very different visions of the final deliverable.
Highlight how you bridged communication gaps and ensured everyone was aligned before implementation.

3.5.10 Tell me about a time you proactively identified a business opportunity through data.
Describe the analysis, how you spotted the opportunity, and the resulting business impact.

4.1 Company-specific tips:

Familiarize yourself deeply with Cohesity’s core business: data management, backup, recovery, and hybrid/multicloud solutions. Understand how the company’s platform consolidates file and object services, analytics, and ransomware protection, and think about how robust data engineering supports these capabilities.

Research Cohesity’s latest innovations in data resilience, security, and automation. Be prepared to discuss how scalable data infrastructure underpins their mission to simplify data management and empower global enterprises.

Review case studies or press releases about Cohesity’s impact on Fortune 500 clients. This will help you connect your technical expertise with real-world business outcomes, demonstrating that you understand how data engineering drives value for Cohesity’s customers.

Learn the basics of Cohesity’s technology stack, especially any public mentions of their use of cloud platforms, distributed systems, and open-source tools. This will help you tailor your answers and show you’re ready to contribute from day one.

4.2 Role-specific tips:

4.2.1 Be ready to design and explain scalable data pipelines for high-volume, heterogeneous data.
Practice articulating your approach to building robust ETL workflows that can handle diverse formats and schema variability. Highlight your strategies for error handling, schema evolution, and modular pipeline design. Be specific about how you would automate ingestion, validate data integrity, and ensure reliability when scaling to enterprise-level volumes.

4.2.2 Demonstrate expertise in optimizing data workflows for both batch and real-time processing.
Prepare to discuss the trade-offs between batch and streaming architectures, especially in scenarios like financial transaction ingestion or hourly analytics. Reference technologies you’ve used (such as Kafka or Spark Streaming), and be ready to explain how you minimize latency, maximize throughput, and guarantee fault tolerance.

4.2.3 Show your ability to design logical and physical data models for warehousing and analytics.
Brush up on normalization, indexing, and schema design principles. Be prepared to walk through the creation of star or snowflake schemas, partitioning strategies, and incremental loading techniques that support efficient business analytics and future growth.

4.2.4 Practice writing and optimizing complex SQL queries for large datasets.
Expect to write queries involving filtering, aggregation, and joining across billions of rows. Discuss your experience with query tuning, indexing, and bulk operations, and explain how you ensure performance and accuracy in high-volume environments.

4.2.5 Illustrate your approach to data quality, cleaning, and integration across multiple sources.
Prepare examples of profiling, cleaning, and validating messy datasets, and be ready to describe how you automate data quality checks and monitor metrics over time. Highlight your process for schema alignment, joining disparate datasets, and extracting actionable insights that improve system performance.

4.2.6 Be prepared to troubleshoot and resolve data pipeline failures systematically.
Talk through your diagnostic process, including log analysis, automated alerts, and dependency mapping. Emphasize your ability to conduct root cause analysis and implement permanent fixes to recurring issues.

4.2.7 Demonstrate familiarity with data engineering tools, programming languages, and system design best practices.
Showcase your hands-on experience with Python and SQL, and discuss scenarios where you’d choose one over the other for performance or maintainability. Be ready to design scalable systems for diverse user needs, integrating cloud platforms and open-source solutions where appropriate.

4.2.8 Prepare strong behavioral stories that showcase teamwork, adaptability, and communication.
Reflect on past experiences where you overcame ambiguity, facilitated stakeholder alignment, or delivered critical insights despite data limitations. Practice articulating your problem-solving approach and the measurable impact of your decisions.

4.2.9 Highlight your ability to automate and improve data reliability.
Share examples of building automated data-quality checks or pipeline monitoring systems that prevented recurring issues. Emphasize your commitment to reproducibility, auditability, and continuous improvement.

4.2.10 Be ready to connect your technical solutions to business outcomes.
Frame your answers to show how your engineering work drives value for Cohesity’s customers—whether by improving data accessibility, enhancing security, or enabling actionable analytics. Show that you understand the bigger picture and are motivated to contribute to Cohesity’s mission.

5.1 How hard is the Cohesity Data Engineer interview?
The Cohesity Data Engineer interview is challenging and rigorous, designed to assess both deep technical expertise and practical problem-solving skills. You’ll encounter questions about scalable data pipeline design, ETL processes, data modeling, and system architecture for high-volume environments. Cohesity values candidates who can build robust, secure, and efficient data infrastructure to support complex enterprise use cases. If you have experience in cloud data platforms, distributed systems, and data quality assurance, you’ll be well-prepared for the technical depth required.

5.2 How many interview rounds does Cohesity have for Data Engineer?
Most candidates can expect 5–6 interview rounds for the Data Engineer role at Cohesity. The process typically includes an initial recruiter screen, one or more technical interviews focused on coding and system design, a behavioral interview, and a final onsite or virtual onsite round with multiple team members. Each round is designed to evaluate specific competencies, from hands-on engineering skills to your ability to communicate and collaborate in a cross-functional environment.

5.3 Does Cohesity ask for take-home assignments for Data Engineer?
Cohesity occasionally includes a take-home technical assignment as part of the Data Engineer interview process, especially to assess real-world problem-solving and coding skills. These assignments often involve designing or implementing data pipelines, writing complex SQL queries, or solving data quality issues. The goal is to evaluate your practical approach, code quality, and ability to communicate your solutions clearly.

5.4 What skills are required for the Cohesity Data Engineer?
Key skills for a Data Engineer at Cohesity include advanced proficiency in SQL and Python, expertise in designing and optimizing ETL workflows, and a deep understanding of distributed data systems and cloud platforms. Experience with data modeling, data warehousing, and integrating diverse data sources is crucial. Cohesity also values skills in data quality assurance, pipeline automation, troubleshooting, and the ability to communicate technical concepts to both technical and non-technical stakeholders.

5.5 How long does the Cohesity Data Engineer hiring process take?
The Cohesity Data Engineer hiring process typically spans 3–4 weeks from application to offer. Each interview round is usually scheduled about a week apart, although the timeline can vary based on candidate availability and team schedules. Candidates with highly relevant experience or internal referrals may progress more quickly, sometimes completing the process in as little as two weeks.

5.6 What types of questions are asked in the Cohesity Data Engineer interview?
You’ll encounter a mix of technical and behavioral questions. Technical questions cover data pipeline design, ETL architecture, SQL and Python coding, data modeling, and system design for scalability and reliability. Expect case studies involving real-time vs. batch processing, troubleshooting pipeline failures, and integrating heterogeneous data sources. Behavioral questions focus on teamwork, communication, problem-solving under ambiguity, and connecting technical work to business outcomes.

5.7 Does Cohesity give feedback after the Data Engineer interview?
Cohesity typically provides feedback through recruiters, especially after final rounds. While feedback may be high-level, you can expect to hear about your strengths and areas for improvement. Detailed technical feedback may be limited, but Cohesity encourages open communication and values candidates who seek to learn and grow from the interview experience.

5.8 What is the acceptance rate for Cohesity Data Engineer applicants?
The Data Engineer position at Cohesity is competitive, with an estimated acceptance rate of 3–5% for qualified applicants. Cohesity seeks candidates who demonstrate both strong technical skills and a clear understanding of how their work supports the company’s mission of simplifying data management for enterprise clients.

5.9 Does Cohesity hire remote Data Engineer positions?
Yes, Cohesity does offer remote positions for Data Engineers, depending on team needs and project requirements. Some roles may require occasional visits to a physical office for team collaboration or onboarding, but Cohesity supports flexible work arrangements for qualified candidates.

• Related guides:
  • Cohesity interview questions
  • Data Engineer interview guide
  • Top data engineering interview tips

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