What is a Data Engineer at Ampersand?

As a Data Engineer at Ampersand, you are at the heart of the company's mission to move TV forward. Ampersand is the industry’s largest source of combined multiscreen TV inventory and viewership insights, representing 118 million multiscreen households and over 75% of addressable households in the U.S. In this role, you will be directly responsible for building the big data pipelines and analytics applications that power these industry-leading insights, fundamentally changing how TV advertising is bought and measured.

Your work will have a massive impact on both local and national advertisers, enabling them to execute true audience-first planning and advanced measurement. Because you will be handling immense volumes of aggregated data insights while rigorously protecting personal information, the technical challenges are highly complex and deeply rewarding. You will be wrangling multi-terabyte datasets, optimizing data systems, and building the analytics tools that provide actionable insights to end users.

This role operates at a senior level of ownership and complexity. You will not just be maintaining existing systems; you will be actively designing and building data pipelines using cutting-edge AWS technologies, Spark, ClickHouse, Scala, and Python. If you are passionate about scale, efficiency, and revolutionizing the Advertising Technology industry, this role offers the perfect environment to grow your career and drive tangible business outcomes.

Common Interview Questions

Getting Ready for Your Interviews

To succeed in the Ampersand interview process, you must demonstrate a strong balance of distributed systems knowledge, hands-on coding ability, and a collaborative mindset. Interviewers will be looking for candidates who can think architecturally while still writing clean, reliable code.

Focus your preparation on the following key evaluation criteria:

• Big Data Architecture & Pipeline Engineering – You will be evaluated on your ability to design robust, scalable data pipelines. Interviewers want to see your deep understanding of the AWS ecosystem and frameworks like Spark and Hadoop, as well as your ability to justify your architectural decisions.
• Advanced Data Wrangling & SQL – Ampersand deals with complex, disparate datasets. You must prove your ability to assemble, combine, and transform large datasets efficiently using advanced SQL and columnar data stores.
• Software Engineering Best Practices – As a data engineer, your code must be efficient, reusable, and reliable. You will be assessed on your proficiency in Python or Scala, your ability to identify bottlenecks, and your dedication to code quality and automation.
• Culture & Values Alignment – Ampersand places a high premium on its core values: Trust, Simplicity, Bravery, Inclusivity, Growth, and Balance. You should be prepared to share examples of how you embody these traits, particularly when navigating ambiguity or collaborating across teams.

Interview Process Overview

The interview process for a Data Engineer at Ampersand is designed to be rigorous yet highly collaborative, reflecting the company’s emphasis on both technical excellence and team fit. You can generally expect the process to begin with a recruiter phone screen, followed by a technical screening round. This initial technical screen typically focuses on your core programming skills (in Python or Scala) and your fundamental SQL capabilities, ensuring you have the baseline coding proficiency required for the role.

If you progress to the virtual onsite stage, the interviews will dive significantly deeper into your specialized knowledge. You will face a series of sessions covering big data pipeline design, deep dives into AWS and Spark architecture, and complex data modeling scenarios. The onsite will also include a dedicated behavioral and values-based interview, where engineering leaders will assess your alignment with Ampersand’s culture and your approach to teamwork and problem-solving.

Throughout the process, interviewers at Ampersand appreciate candidates who communicate clearly, ask clarifying questions, and default to simplicity when designing solutions. They are looking for engineers who can not only build complex systems but also explain the "why" behind their technical choices.

This visual timeline outlines the typical stages of the Ampersand interview loop, from initial screening to the final behavioral rounds. Use this to structure your preparation, ensuring you review core programming and SQL early on, while reserving time to practice complex system design and pipeline architecture for the final onsite stages.

Deep Dive into Evaluation Areas

To excel in your interviews, you need a deep understanding of the specific technical and behavioral domains that Ampersand prioritizes. The evaluation will test the limits of your practical experience with big data at scale.

Big Data Ecosystems & AWS Architecture

Ampersand relies heavily on the AWS ecosystem and distributed computing frameworks to process viewership insights for tens of millions of households. Interviewers will evaluate your practical experience with these tools, looking for candidates who understand how to optimize performance and manage costs at scale. Strong performance means you can confidently discuss the internal mechanics of distributed processing, rather than just knowing the high-level APIs.

Be ready to go over:

• Apache Spark – Partitioning strategies, handling data skew, memory management, and optimizing shuffles.
• AWS Data Tools – Practical usage of EMR, Athena, S3, and Data Pipeline, including how to secure and manage access via IAM.
• Containerization & Orchestration – Utilizing EKS (Elastic Kubernetes Service) for deploying and scaling data applications.
• Advanced concepts (less common) – Integrating ClickHouse for real-time analytics, or managing complex cluster scaling policies.

Example questions or scenarios:

• "Walk me through how you would optimize a highly skewed Spark job running on AWS EMR."
• "Design a data pipeline that ingests daily viewership logs from S3, transforms them, and makes them available for low-latency querying via Athena."

Advanced SQL & Columnar Data Stores

Given the volume and analytical nature of the data at Ampersand, standard relational database knowledge is not enough. You will be tested on your ability to work with advanced SQL and columnar storage formats. Interviewers want to see that you understand how data layout impacts query performance and how to model data for downstream analytics tools.

Be ready to go over:

• Columnar Formats – The benefits of Parquet over row-based formats, and how to optimize file sizes and compression.
• Query Engines – Experience with Presto, Athena, or Snowflake, and understanding how distributed query engines execute SQL.
• Complex Transformations – Using window functions, CTEs, and complex joins to assemble multiple disparate ad-tech datasets.
• Advanced concepts (less common) – Designing data models specifically for addressable TV advertising metrics.

Example questions or scenarios:

• "Explain the difference between a broadcast join and a shuffle hash join, and when you would use each."
• "Write an advanced SQL query to calculate the rolling 7-day unique viewership for a specific advertising campaign across multiple regions."

Programming & Code Quality

A Data Engineer at Ampersand is expected to be a strong software engineer. You will be evaluated on your ability to write clean, reusable, and efficient code in Scala or Python. Interviewers will look for your ability to identify bugs, mitigate bottlenecks, and implement robust testing and automation practices.

Be ready to go over:

• Data Structures & Algorithms – Standard coding fundamentals, focusing on string manipulation, dictionaries/hash maps, and list processing.
• Object-Oriented & Functional Programming – Utilizing the right paradigm for the task, especially when using Scala with Spark.
• Code Organization – How you structure repositories, manage dependencies, and ensure code is reliable and maintainable.
• Advanced concepts (less common) – Building custom UDFs (User Defined Functions) in Spark to handle complex, domain-specific logic.

Example questions or scenarios:

• "Write a Python function to parse a complex, nested JSON payload representing a user's multiscreen viewing session."
• "How do you approach testing a data pipeline to ensure data quality and catch regressions before they hit production?"