What is a Data Engineer at Tessian?

As a Data Engineer at Tessian, you are at the core of a mission-critical operation: securing the human layer of enterprise communication. Tessian relies heavily on advanced machine learning to detect and prevent security threats like data exfiltration, phishing, and accidental data loss. To make these predictive models effective, the underlying data infrastructure must be exceptionally robust, scalable, and secure. You will be responsible for building the pipelines that process massive volumes of sensitive email and communication data in real time.

The impact of this position extends across multiple products and directly influences the business's bottom line. By designing fault-tolerant data architectures, you empower the Data Science and Engineering teams to deploy smarter, faster models. Your work ensures that data flows seamlessly from ingestion to inference, maintaining strict compliance and privacy standards along the way. At Tessian, data engineering is not just about moving data; it is about enabling intelligent security solutions that protect millions of users.

Expect a role that balances deep technical complexity with high strategic influence. You will tackle challenges related to distributed computing, real-time stream processing, and large-scale system design. The environment is fast-paced and highly collaborative, requiring you to bridge the gap between raw data and actionable security intelligence.

Common Interview Questions

The questions below are representative of what candidates experience during the Tessian interview process. They are drawn from actual interview reports and are meant to illustrate the patterns and depth of inquiry you will face. Do not memorize answers; instead, use these to practice your problem-solving frameworks and project storytelling.

Algorithmic and Coding Questions

These questions test your core computer science fundamentals and your ability to write clean, optimized code under time constraints.

• Write a function to identify the most frequent IP addresses in a large log file.
• Given a list of user login timestamps, implement an algorithm to detect if a user has logged in from two different locations within an impossible timeframe.
• Solve a dynamic programming problem involving optimizing the scheduling of data processing jobs.
• Implement a custom data structure that supports fast insertion, deletion, and retrieval of randomized elements.

System Design and Architecture

These questions assess your ability to design scalable, fault-tolerant data systems from the ground up.

• Design a real-time data pipeline to ingest, process, and store millions of email metadata events per minute.
• How would you architect a system to guarantee exactly-once processing in a distributed streaming environment?
• Walk me through the database schema you would design for a system that tracks user communication graphs over time.
• Explain how you would handle schema evolution in a large-scale data warehouse without disrupting downstream analytics.

Practical Experience and Project Deep Dives

Interviewers use these questions to validate your resume and understand your hands-on engineering experience.

• Describe a time when a data pipeline you built failed in production. How did you troubleshoot and resolve the issue?
• Walk me through a project where you had to significantly optimize the performance or cost of an existing data system.
• How have you previously collaborated with data scientists to deploy machine learning models into production?
• Explain a complex architectural decision you made recently. What were the trade-offs, and would you make the same choice today?

Behavioral and Values

These questions gauge your cultural alignment, leadership potential, and ability to navigate workplace challenges.

• Tell me about a time you had to push back on a requirement from a stakeholder because it was technically unfeasible.
• Describe a situation where you had to learn a completely new technology on the fly to deliver a project.
• How do you prioritize tasks when you receive competing urgent requests from different teams?
• Why are you interested in joining Tessian, and how does our mission in cybersecurity resonate with your career goals?

Getting Ready for Your Interviews

Preparing for a Data Engineer interview at Tessian requires a strategic approach. The hiring team values candidates who can write efficient code, design scalable systems, and articulate the business impact of their past projects. Your preparation should focus on demonstrating a blend of algorithmic proficiency and practical engineering sense.

Interviewers will evaluate you against several key criteria:

• Role-related knowledge – This evaluates your command of core data engineering technologies, including Python, SQL, distributed systems, and cloud infrastructure. Interviewers want to see that you can choose the right tools for complex data pipelines.
• Problem-solving and Algorithms – Tessian places a strong emphasis on your ability to break down complex problems and write optimized code. You will be tested on your algorithmic thinking and how you structure your logic under constraints.
• System Design and Architecture – This assesses your ability to design end-to-end data systems that are scalable, reliable, and secure. You must demonstrate how you handle data modeling, batch versus stream processing, and fault tolerance.
• Practical Experience and Execution – Interviewers will dive deep into your resume to understand how you have delivered value in the past. You should be prepared to discuss the architecture, challenges, and outcomes of your previous projects.
• Culture Fit and Values – Tessian looks for candidates who are collaborative, adaptable, and aligned with their core mission. You will be evaluated on your communication skills and how well you navigate ambiguity and teamwork.

Interview Process Overview

The interview process for a Data Engineer at Tessian is designed to be clear, fair, and highly practical. It moves efficiently from initial mutual discovery to rigorous technical evaluations, culminating in leadership and values discussions. The company is transparent about expectations, and the recruitment team is known to be supportive—even discussing visa and relocation options right from the initial phone screen.

You can expect a balanced mix of conversational deep-dives and hands-on technical assessments. Rather than relying solely on abstract whiteboard puzzles, Tessian focuses heavily on your actual engineering experience and how you apply algorithms and system design to realistic scenarios. The technical rigor is high, particularly during the dedicated coding assessments, but the conversations remain grounded in practical application.

One distinctive aspect of the Tessian process is the cross-functional nature of the later rounds. You will speak directly with both engineering managers and data science leaders, reflecting the highly collaborative nature of the role. The process wraps up with a dedicated values interview with executive leadership, underscoring how deeply the company cares about cultural alignment and mission focus.

This visual timeline outlines the progression from your initial recruiter screen through the technical assessments and final leadership interviews. Use it to pace your preparation, ensuring you are ready for the intensive two-hour technical exercise early on, while saving energy for the deep architectural and behavioral discussions in the final stages.

Deep Dive into Evaluation Areas

To succeed, you must demonstrate proficiency across several distinct technical and behavioral domains. The interviews are structured to test both your theoretical knowledge and your practical execution.

Algorithms and Data Structures

• Why it matters: Handling massive datasets requires code that is highly optimized for time and space complexity. Tessian needs engineers who can write efficient algorithms that process data at scale without bottlenecking the system.
• How it is evaluated: You will face algorithmic questions during your initial manager interview and as part of a rigorous two-hour HackerRank exercise. The focus is on correctness, efficiency, and clean code structure.
• What strong performance looks like: A strong candidate quickly identifies the optimal data structures (e.g., hash maps, graphs, trees) and algorithms (e.g., dynamic programming, sliding window) for the problem. They communicate their thought process clearly before writing code and proactively discuss edge cases.

Be ready to go over:

• Array and String Manipulation – Core operations, parsing, and data cleaning.
• Graph Algorithms – Useful for mapping relationships in communication data.
• Sorting and Searching – Optimizing data retrieval processes.
• Advanced concepts (less common) – Trie structures for text prefix matching, advanced dynamic programming for sequence alignment.

Example questions or scenarios:

• "Write an algorithm to detect anomalous communication patterns in a stream of email metadata."
• "Given a large dataset of user interactions, how would you efficiently find the top K most active users?"
• "Implement a solution to merge overlapping intervals of data processing jobs."

System Design and Data Architecture

• Why it matters: A Data Engineer must build pipelines that are resilient, scalable, and secure. At Tessian, your designs directly impact the performance of machine learning models that protect clients.
• How it is evaluated: System design is evaluated both within the HackerRank exercise (which uniquely tests design alongside algorithms) and during your interviews with engineering leadership.
• What strong performance looks like: You should be able to draw clear boundaries between ingestion, storage, processing, and serving layers. Strong candidates discuss trade-offs between batch and stream processing, justify their database choices, and explain how they would monitor the system for data drift or pipeline failures.

Be ready to go over:

• Data Modeling – Schema design for both relational and NoSQL databases.
• Pipeline Architecture – Designing ETL/ELT processes using tools like Spark, Kafka, or Airflow.
• Scalability and Fault Tolerance – Ensuring the system recovers gracefully from node failures or data spikes.
• Advanced concepts (less common) – Designing privacy-preserving data pipelines, managing data lineage in complex ML architectures.

Example questions or scenarios:

• "Design a real-time data ingestion pipeline that feeds email metadata into a machine learning classification model."
• "How would you architect a system to handle sudden, massive spikes in data volume without losing any events?"
• "Explain how you would design a data warehouse schema to support both daily reporting and ad-hoc data science queries."

Practical Experience and Project Deep Dives

• Why it matters: Tessian values engineers who have a track record of delivering real-world impact. They want to know not just what technologies you used, but why you used them and what business value they unlocked.
• How it is evaluated: Expect a highly practical interview with the Data Science Lead and Engineering Manager. They will ask detailed questions about the projects listed on your resume.
• What strong performance looks like: You can confidently explain the architecture of your past projects, the technical hurdles you overcame, and the metrics of your success. You take ownership of your work and can articulate the broader business context.

Be ready to go over:

• Architecture Decisions – Why you chose a specific framework or database over another.
• Performance Optimization – Specific examples of how you reduced latency or costs in a pipeline.
• Cross-functional Collaboration – How you worked with data scientists or product managers to define requirements.

Example questions or scenarios:

• "Walk me through the most complex data pipeline you have built. What were the primary bottlenecks?"
• "Tell me about a time you had to compromise on a technical design to meet a business deadline."
• "How did you ensure data quality and accuracy in your previous projects?"

Values and Culture Fit

• Why it matters: Technical brilliance is not enough if you cannot work effectively within the team. Tessian is a mission-driven company, and leadership wants to ensure you align with their core values and collaborative culture.
• How it is evaluated: This is typically assessed in a dedicated 30-minute interview with a senior executive, such as the CFO.
• What strong performance looks like: You demonstrate humility, a strong sense of ownership, and a passion for the company's mission in cybersecurity. You provide structured, reflective answers using the STAR method (Situation, Task, Action, Result) when discussing past behavioral challenges.

Key Responsibilities

As a Data Engineer at Tessian, your day-to-day work revolves around building and maintaining the infrastructure that powers the company's intelligent security products. You will design, develop, and optimize scalable data pipelines that ingest massive streams of communication data. Your primary deliverable is clean, reliable, and accessible data that the rest of the organization can trust.

A significant portion of your role involves close collaboration with adjacent teams. You will partner extensively with the Data Science team to understand their model requirements, ensuring your pipelines deliver the right features at the right latency. You will also work alongside backend engineers to integrate your data solutions into the core product architecture, and with operations to monitor pipeline health and troubleshoot performance bottlenecks.

You will drive initiatives related to data quality, system scalability, and security compliance. Typical projects might include migrating legacy batch jobs to real-time streaming architectures, implementing robust data monitoring and alerting systems, or designing privacy-first data storage solutions that comply with strict enterprise security standards.

Role Requirements & Qualifications

To be competitive for the Data Engineer position at Tessian, you need a strong foundation in software engineering applied to data systems. The role demands a mix of coding proficiency, architectural vision, and collaborative soft skills.

• Must-have skills – Exceptional proficiency in Python and SQL. You must have deep experience with building ETL/ELT pipelines and working with distributed computing frameworks (such as Apache Spark). A solid understanding of cloud platforms (AWS is highly preferred) and relational/NoSQL databases is essential.
• Nice-to-have skills – Experience with stream processing tools like Apache Kafka or Flink. Familiarity with orchestration tools like Apache Airflow. Background in cybersecurity or experience building pipelines specifically for machine learning models will make you a standout candidate.
• Experience level – Typically requires 3 to 5+ years of dedicated data engineering or backend software engineering experience, preferably in high-growth tech environments or handling large-scale data systems.
• Soft skills – Strong cross-functional communication is critical. You must be able to translate complex data science requirements into robust engineering tasks and clearly articulate architectural trade-offs to non-technical stakeholders.

Frequently Asked Questions

Q: How difficult is the interview process for a Data Engineer at Tessian? The difficulty is generally rated as Medium to Hard. The process is straightforward and fair, but the two-hour HackerRank exercise is rigorous and requires a solid grasp of both algorithms and system design principles.

Q: How much preparation time is typical for this role? Most successful candidates spend 2 to 4 weeks preparing. You should divide your time evenly between practicing algorithmic coding, reviewing system design frameworks, and structuring the narratives of your past projects.

Q: What makes a candidate stand out in the practical interviews? Candidates who can clearly connect their engineering work to business outcomes stand out. Tessian interviewers appreciate engineers who understand why a pipeline was built, not just how it was built.

Q: Does Tessian offer visa and relocation support? Yes, recruiters have explicitly discussed visa sponsorship and relocation options during initial phone screens for this role, making it an accessible opportunity for international candidates.

Q: What should I expect in the final interview with the CFO? This is a values and culture fit interview. Expect behavioral questions focused on your work ethic, adaptability, and alignment with Tessian's mission. It is a conversation about your long-term trajectory within the company.

Other General Tips

• Master the STAR Method: When discussing your past projects with the Engineering Manager and Data Science Lead, structure your answers clearly. Define the Situation and Task, detail the specific Actions you took, and quantify the Results.
• Manage Your Time on the HackerRank: The two-hour technical exercise is comprehensive. Do not get stuck on a single algorithmic hurdle. Ensure you allocate enough time to demonstrate your system design thinking, as the assessment evaluates both areas.

• Understand the Machine Learning Context: While you are interviewing for a Data Engineer role, your primary "customers" internally will be Data Scientists. Brush up on basic ML concepts so you can speak intelligently about feature stores, model training pipelines, and inference latency.
• Emphasize Security and Privacy: Tessian is a cybersecurity company. Whenever discussing system design or data architecture, proactively mention how you would handle encryption, access controls, and data anonymization. This demonstrates strong domain awareness.
• Ask Insightful Questions: Use the end of your interviews to ask about Tessian's data volume, their current infrastructure bottlenecks, or how the engineering team collaborates with product managers. This shows genuine interest and strategic thinking.

Summary & Next Steps

Securing a Data Engineer role at Tessian is a unique opportunity to build high-scale data infrastructure that directly protects enterprise security. The role demands a rigorous blend of algorithmic thinking, robust system design, and a deep appreciation for data quality and privacy. The interview process is designed to be highly practical, focusing on your ability to execute complex projects and collaborate effectively across engineering and data science teams.

To succeed, focus your preparation on mastering core data structures, designing fault-tolerant pipelines, and articulating the business impact of your past work. Approach the two-hour technical assessment with a focus on clean, scalable code, and treat the leadership interviews as an opportunity to showcase your passion for the company's mission.

This compensation data provides a baseline expectation for the role. Keep in mind that total compensation at Tessian may include a mix of base salary, equity, and benefits, and will vary based on your specific experience level and location.

You have the skills and the roadmap to excel in this process. Continue to refine your technical communication, practice your system design frameworks, and explore additional interview insights on Dataford to sharpen your edge. Approach your interviews with confidence, knowing that focused, strategic preparation will significantly elevate your performance.