Data Engineer Ii

Data Engineer II at Magnite — Research Report

Introduction

The Data Engineer II role at Magnite is a strong fit for candidates who want to build the data systems behind modern digital advertising. This job matters because Magnite sits in the infrastructure layer of programmatic media, where data quality, pipeline reliability, and fast analytics directly affect revenue and product performance. The posting details indicate a remote or hybrid setup tied to New York, NY, and the deadline is not mentioned, so applicants should move quickly once a role appears live.

If you are looking for how to get hired at Magnite, this role offers a useful path: it combines practical engineering, high-volume data, and cross-functional business impact. It is especially attractive for candidates who want to sharpen cloud data skills, learn the ad tech ecosystem, and work on systems that influence real-time decisions at scale.

Overview of Magnite

Magnite is a major independent advertising technology company focused on sell-side platform services, which means it helps publishers and media owners monetize digital inventory across connected TV, online video, display, and mobile. In simple terms, the company provides the infrastructure that helps ad impressions get bought and sold efficiently in programmatic markets.

Magnite is known for operating in a niche that sits between publishers, advertisers, and demand-side platforms. Its competitors include other ad tech and supply-side platform players such as PubMatic, Index Exchange, and The Trade Desk’s broader ecosystem, though Magnite’s positioning is especially strong in connected TV and omnichannel video. That specialization matters because ad spend continues to shift toward streaming environments, where clean data and latency-sensitive infrastructure are essential.

The company’s product set includes tools and platforms for programmatic ad selling, yield optimization, audience activation, and reporting. For a Data Engineer II, that means the work likely supports data ingestion, transformation, storage, and analytics used by operations, product, and revenue teams. Magnite’s reputation in the market comes from scale, technical complexity, and its role in a fast-moving industry where measurement and monetization depend on well-architected systems.

Culture-wise, Magnite tends to appeal to people who like ownership, technical depth, and practical execution. The work environment is usually attractive to engineers who want to see their output used quickly by business teams. That combination of scale, visibility, and ad tech relevance is a big reason people want to work there.

Data Engineer II Role

Role Overview

A Data Engineer II at Magnite is typically responsible for building and maintaining reliable data pipelines that support analytics, reporting, product insight, and operational decision-making. The role sits at the intersection of engineering and business intelligence, so the output is not just technical cleanliness but also measurable business value.

Day to day, the engineer may work with high-volume event data, advertiser or publisher activity, and product telemetry. The goal is to make data usable, trusted, and timely enough for teams to make decisions about monetization, platform performance, and customer outcomes.

Detailed Responsibilities

• Design, build, and maintain ETL and ELT pipelines for structured and semi-structured data.
• Ingest data from internal services, third-party APIs, logs, and streaming sources.
• Improve pipeline reliability, data freshness, and error handling.
• Write SQL transformations and optimize queries for large datasets.
• Partner with analysts, product managers, and engineers to define data models and metrics.
• Monitor data quality, consistency, and lineage across systems.
• Support warehouse and lakehouse architecture for reporting and experimentation.
• Document schemas, workflows, and operational dependencies for long-term maintainability.
• Troubleshoot production data issues and participate in incident response when needed.

Day-to-Day Workflow

A realistic day for this role often starts with checking pipeline health, reviewing failed jobs, and scanning alerts or logs. From there, the engineer may spend time fixing a broken transformation, tuning a query, or adding a new data source to a warehouse table. A later block of the day may involve pairing with an analyst to clarify a metric definition or working with a product engineer to ensure event tracking is accurate.

Because Magnite operates in a data-heavy industry, the job is rarely just one thing. Some days are maintenance-heavy, while others focus on building new datasets for reporting, experimentation, or customer-facing analytics. Strong engineers in this role tend to balance speed with precision, because mistakes in ad tech data can ripple into pricing, forecasting, and attribution workflows.

Tools and Technologies

While Magnite’s exact stack can vary by team, a Data Engineer II in this environment is likely to work with a cloud data platform, SQL, Python, orchestration tools, and warehouse technologies. Common tools in roles like this include Snowflake, BigQuery, Redshift, Airflow, dbt, Databricks, Spark, Kafka, and object storage such as S3.

Monitoring and collaboration tools also matter, especially for production support and cross-functional work. Candidates should be comfortable with Git, CI/CD concepts, Jira, and logging or observability platforms. In ad tech, familiarity with event-driven architecture and large-scale distributed systems is often a major advantage.

Skills and Requirements

Technical Skills

To compete for a Data Engineer II role at Magnite, you need more than basic database knowledge. Employers in this space usually want strong SQL, solid Python, experience with data modeling, and practical familiarity with cloud warehouses and orchestration frameworks.

• Advanced SQL for joins, window functions, query tuning, and transformation logic.
• Python for scripting, automation, API work, and pipeline development.
• Experience with ETL/ELT workflows and orchestration tools like Airflow.
• Understanding of dimensional modeling, star schemas, and analytics-ready tables.
• Familiarity with cloud platforms and warehouse systems such as Snowflake, Redshift, or BigQuery.
• Basic knowledge of distributed processing tools like Spark or Databricks is a plus.
• Awareness of data quality checks, schema evolution, and production monitoring.

Soft Skills

Magnite will likely value engineers who can explain technical tradeoffs without overcomplicating them. This role lives close to product and analytics teams, so communication matters as much as code quality. Candidates who can translate business questions into clean datasets usually stand out quickly.

• Clear written communication for documentation and requirement gathering.
• Collaborative problem-solving with analysts, engineers, and stakeholders.
• Ownership mentality for resolving pipeline issues and following through.
• Ability to prioritize work when multiple data requests compete for attention.
• Comfort working in a fast-moving, cross-functional environment.

Experience Expectations

Although the title is Data Engineer II, this level usually suggests a few years of relevant experience or equivalent project depth. A strong candidate may have built real pipelines, shipped analytics tooling, or contributed to production systems through internships, co-ops, or full-time roles.

Hiring teams often look for a portfolio that proves you can work with real data, not just classroom examples. GPA may matter for early-career applicants, but it is usually less important than evidence of technical execution, internship experience, and the ability to explain your decisions clearly.

Salary and Benefits

For New York-based data engineering roles at a company like Magnite, a realistic salary range for Data Engineer II is often around $115,000 to $155,000 base pay, with total compensation potentially higher depending on bonus and equity structure. Remote or hybrid arrangements can also make the role more appealing to candidates who want flexibility without leaving a major market like New York behind.

Typical benefits may include medical coverage, paid time off, retirement support, wellness programs, and access to learning resources. In tech-adjacent companies, engineers may also receive budget support for conferences, certifications, or professional development. If the role is tied to a broader talent pipeline, there may be opportunities to move into senior engineering, analytics engineering, or platform-focused positions over time.

Magnite Hiring Process

Step-by-Step Hiring Stages

1. Application
2. Screening
3. Assignment
4. Interviews
5. Offer

The process usually begins with an online application and recruiter review. If the resume aligns with the role, candidates often move into a screening call, followed by a technical assignment or live technical evaluation, then a series of interviews with engineering and cross-functional stakeholders.

Application Timeline

Because the deadline is not mentioned in the role details, applicants should assume the process is competitive and time-sensitive. For open tech roles like this, companies often review candidates on a rolling basis, so submitting early is a real advantage. A typical process can take two to five weeks depending on interviewer availability and the speed of the technical round.

Screening Methods

Magnite’s screening is likely to focus on resume keywords, relevant project history, and evidence of production-grade data work. Applicant tracking systems usually favor clear references to SQL, Python, ETL, cloud platforms, orchestration, and data modeling. A concise portfolio or GitHub repository can help, especially if it shows end-to-end thinking rather than isolated scripts.

Interview Preparation

Example Interview Questions

• How would you design a pipeline to ingest high-volume ad event data in near real time?
• Tell us about a time you fixed a production data issue with incomplete or inconsistent records.
• How do you decide between a batch pipeline and a streaming pipeline?
• How would you optimize a slow SQL query used for dashboard reporting?

How to Answer

Use the STAR method for behavioral questions: describe the situation, your task, the action you took, and the result. For technical questions, start by clarifying the business need, then explain your architecture or logic in steps. Interviewers at a company like Magnite want to see structured thinking, not just technical vocabulary.

When discussing your work, quantify the outcome whenever possible. Talk about latency reduction, failure-rate improvement, query performance gains, or the size of the datasets you handled. Specific numbers make your experience feel real and credible.

What Recruiters Evaluate

Recruiters and hiring managers usually evaluate whether you can ship reliable data work without heavy supervision. They also want evidence that you understand how data supports business decisions in ad tech, where accuracy and speed both matter. If you can show that you care about data quality, collaboration, and maintainability, you will already be ahead of many applicants.

How to Get Selected

Practical Tips

• Tailor your resume to emphasize SQL, Python, data pipelines, and warehouse experience.
• Include one or two projects that show end-to-end data engineering, not just analysis.
• Use metrics in every relevant bullet point to show scale and impact.
• Study ad tech basics so you can speak intelligently about publishers, impressions, and programmatic workflows.
• Prepare a concise story about a time you improved reliability or solved a messy data issue.

Common Mistakes to Avoid

One of the biggest mistakes candidates make is applying with a generic software or analytics resume that does not reflect data engineering work. Another common issue is listing tools without showing how they were used in production. Recruiters notice when a resume says “Python” and “SQL” but never explains the pipeline, dataset, or result.

Applicants also get rejected when they cannot explain tradeoffs. If you built something in a class or internship, be ready to discuss why you chose that architecture, what failed, and what you would improve next time. That kind of reflection signals maturity.

How to Stand Out

The strongest candidates usually bring a portfolio that feels close to the actual job. A good example is a project that ingests raw API data, cleans it with Python, transforms it with SQL or dbt, and loads it into a warehouse with a dashboard on top. That gives hiring teams a direct look at how you think through reliability and usability.

Networking helps too, especially if you can connect with current or former employees on LinkedIn and ask thoughtful questions about the engineering culture. Tailored submissions also matter: mention Magnite’s ad tech focus, the importance of scalable data systems, and why you want to work in media monetization. Those details show intent, not just interest.

Final Thoughts

The Data Engineer II role at Magnite is a strong opportunity for candidates who want to grow in a technically demanding, business-critical environment. It combines solid engineering fundamentals with real exposure to digital advertising, which makes it especially valuable for long-term career growth in data infrastructure and analytics engineering.

If you are serious about landing this role, focus on proving that you can build reliable systems, communicate clearly, and solve messy data problems with confidence. A targeted resume, a sharp technical portfolio, and a thoughtful interview story can make a real difference in how to get hired at Magnite.

Frequently Asked Questions

Salary at Magnite

What is the salary for Data Engineer II at Magnite?

A realistic New York market estimate is about $115,000 to $155,000 in base salary, with total compensation potentially increasing through bonus or equity depending on the package.

Hiring Difficulty

How competitive is it to get hired at Magnite?

It is moderately to highly competitive because Magnite sits in a specialized, technical segment of ad tech. Candidates who show strong SQL, Python, and production data experience usually have the best odds.

Top Skills

What skills are most important for this role?

The most important skills are SQL, Python, ETL pipeline design, data modeling, cloud warehouse experience, and the ability to communicate clearly with technical and non-technical teammates.