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.

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?"

Key Responsibilities

As a Data Engineer at Ampersand, your day-to-day work will revolve around building and optimizing the foundational data systems that drive the business. You will spend a significant portion of your time designing and implementing data pipelines using AWS EMR, Spark, Scala, and Python. This involves writing the code that extracts raw viewership and inventory data, transforms it to enforce business logic and privacy standards, and loads it into columnar data stores like Parquet or Snowflake.

Collaboration is a massive part of this role. You will work closely with data scientists, product managers, and software engineering teams to understand their data needs and build analytics tools that provide actionable insights. When a new advertising measurement feature is proposed, you will be the one figuring out how to assemble, combine, and transform the large, complex datasets required to support it.

Beyond building new features, you will take ownership of system reliability. You will actively monitor data pipelines to identify bottlenecks, troubleshoot bugs, and devise elegant solutions to mitigate issues. Maintaining code quality through rigorous peer reviews, organization, and automated testing will be a daily priority, ensuring that Ampersand's data infrastructure remains scalable and resilient as the company continues to grow.

Role Requirements & Qualifications

To be a competitive candidate for the Data Engineer position at Ampersand, you must bring a mix of deep technical expertise and strong software engineering fundamentals. The ideal candidate has significant experience operating in modern, cloud-native big data environments.

• Must-have skills – You must have extensive experience building and optimizing big data pipelines using frameworks like Spark and Hadoop. A strong command of the AWS ecosystem (S3, Athena, EMR, EC2, IAM) is strictly required. Furthermore, you must possess advanced SQL knowledge and strong coding proficiency in Python and/or Scala. Experience with columnar data stores like Parquet, Presto, or Snowflake is also non-negotiable.
• Nice-to-have skills – Familiarity with container orchestration via EKS and high-performance analytical databases like ClickHouse will make your profile stand out. Previous experience in the Advertising Technology industry, specifically dealing with TV inventory or viewership data, is a strong plus.
• Experience level – While the job title is Data Engineer, the responsibilities strongly align with a Senior Data Engineer profile. You should typically have 5+ years of dedicated data engineering experience, demonstrating a track record of owning complex data systems from design to production.
• Soft skills – You must be a clear communicator who can translate complex data concepts to non-technical stakeholders. A strong alignment with Ampersand's core values—particularly Trust, Simplicity, and Bravery—is essential for thriving in this collaborative environment.

Common Interview Questions

While the exact questions you face will depend on your interviewers, reviewing common patterns will help you solidify your approach. The following questions are representative of what candidates encounter for data engineering roles focused on AWS and big data at scale.

Big Data & AWS Architecture

This category tests your ability to design resilient, scalable systems using modern cloud infrastructure.

• Design a data pipeline on AWS to process 5TB of daily log data, ensuring it is queryable within an hour of arrival.
• How does Spark handle fault tolerance, and how would you configure an EMR cluster to maximize resilience and minimize cost?
• Explain the architecture of Athena. When would you choose to query data via Athena versus loading it into a dedicated data warehouse?
• Tell me about a time you had to identify and resolve a severe performance bottleneck in a distributed data pipeline.
• How do you manage data partitioning in S3 to optimize downstream reads for Spark and Presto?

Advanced SQL & Data Modeling

These questions evaluate your ability to manipulate complex datasets and understand the underlying mechanics of analytical databases.

• Given a table of user viewing sessions (user_id, start_time, end_time, channel), write a query to find the peak concurrent viewership for a given day.
• Explain how columnar storage formats like Parquet work under the hood. Why are they preferred for big data analytics?
• Write a query to identify the top 3 most-watched programs per region, utilizing window functions.
• How do you handle slowly changing dimensions (SCDs) in a cloud data warehouse?
• Describe a complex dataset you had to assemble from multiple disparate sources. How did you ensure data integrity during the join process?

Coding & Software Engineering

This area assesses your programming fundamentals, usually in Python or Scala, and your dedication to code quality.

• Write a Python script to interact with the AWS S3 API, list all files in a bucket matching a specific prefix, and filter out files older than 30 days.
• In Scala, explain the difference between a val, var, and def. How does functional programming benefit data engineering?
• Implement a function to merge two overlapping intervals of time (representing ad viewing windows).
• How do you structure your code to ensure it is reusable across multiple different Spark jobs?
• Walk me through your approach to writing unit tests for data transformations.

Behavioral & Values

These questions check your alignment with Ampersand's culture and your ability to work effectively in a team.

• Ampersand values "Simplicity." Tell me about a time you replaced a highly complex technical process with a much simpler one.
• Describe a situation where you had to show "Bravery" by pushing back on a technical requirement that you knew was not scalable.
• How do you balance the need to deliver actionable insights quickly with the need to maintain robust, reliable code?
• Tell me about a time you mentored a junior engineer or helped improve your team's overall code quality.
• Describe your approach to building an inclusive environment within your engineering team.

Frequently Asked Questions

Q: How difficult are the technical interviews for this role? The technical rounds are rigorous and focus heavily on practical application rather than academic trivia. Expect deep dives into Spark internals, AWS architecture, and complex SQL. Preparation should focus on real-world scenarios you've encountered and how you solved them at scale.

Q: Does Ampersand require me to be in the office? This role is located in New York, NY. Ampersand generally operates on a hybrid model, balancing in-person collaboration with the flexibility of remote work. Be prepared to discuss your location and working style preferences with your recruiter.

Q: What makes a candidate stand out in the interview process? Candidates who stand out do more than just write code; they understand the business context. Demonstrating an understanding of ad-tech, viewership metrics, and how data drives advertising decisions will significantly elevate your profile above candidates who only focus on the tech stack.

Q: How much preparation time is typical for this interview loop? Most successful candidates spend 2 to 3 weeks preparing. Dedicate the first week to brushing up on Python/Scala coding and advanced SQL, and use the subsequent weeks to practice whiteboarding data pipeline architectures and reviewing AWS and Spark specifics.

Q: What is the culture like on the engineering team? The culture is highly collaborative and driven by the company's core values. There is a strong emphasis on "Trust" and "Balance," meaning you are given the autonomy to solve complex problems while maintaining a healthy work-life balance.

Other General Tips

• Master Columnar Storage Concepts: You must be able to explain exactly why and how Parquet works. Understand row groups, column chunks, dictionary encoding, and predicate pushdown. This is critical for roles utilizing Athena and Presto.
• Speak the Language of Ad-Tech: While you don't need to be an advertising expert, familiarizing yourself with terms like "multiscreen inventory," "addressable households," and "audience-first planning" will help you contextualize your system design answers.

• Review Your Spark Tuning Strategies: Questions about handling data skew, OutOfMemory (OOM) errors, and optimizing shuffles are almost guaranteed. Be prepared to discuss specific configurations you've tweaked in the past.
• Highlight Reusability: The job description explicitly mentions "Skill for writing reusable code." During your coding rounds, don't just solve the problem—refactor your solution to show how it could be modularized and reused across different pipelines.

• Brush up on EKS and ClickHouse: While Spark and AWS are the core, the mention of EKS and ClickHouse indicates a modern, containerized, and real-time analytical environment. Understanding the basics of Kubernetes orchestration and ClickHouse's analytical capabilities will give you a competitive edge.

Summary & Next Steps

The compensation module above provides a look at the expected base salary range for this position in New York, which spans from $110,000 to$150,000. Keep in mind that this base is just one component of Ampersand’s total rewards package, which also includes a discretionary annual bonus, 401k matching, and comprehensive health benefits.

Interviewing for a Data Engineer at Ampersand is an exciting opportunity to join a company that is actively revolutionizing the Advertising Technology space. By focusing your preparation on big data architecture, advanced SQL, Spark optimization, and writing clean, reusable code, you will position yourself as a strong contender. Remember that the interviewers are not just looking for a coder; they want a strategic partner who can build robust pipelines to power the future of TV measurement.

Approach your preparation systematically, practice articulating your architectural decisions clearly, and let your passion for data engineering shine through. For more deep dives into specific technical questions and peer experiences, continue exploring the resources available on Dataford. You have the skills and the drive—now it's time to showcase them. Good luck!