Want to get hired at Netflix?

Analytics Engineer - Ads

Netflix

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Original Job Summary

About Netflix

Netflix is one of the world's leading entertainment services, with 283 million paid memberships in over 190 countries. Members enjoy a wide variety of TV series, films, and games anytime, anywhere.

Role Overview

As a Senior Analytics Engineer - Ads at Netflix, you will design metrics, generate insights from deep dive analyses, and provide strategic recommendations based on product experiments. You will work closely with partner teams in this nascent ads business area.

Responsibilities

  • Identify opportunities to automate ad hoc requests.
  • Create metrics for decision-making and insight.
  • Develop dashboards and automate reporting systems.
  • Establish data pipelines for business self-service.

Qualifications

  • Expert SQL skills, programming (Python, Scala) and basic ETL/domain exposure.
  • Proven record in analytics, reporting and visualization (Tableau, D3).
  • Strong communication and stakeholder relationship skills.
  • Ability to innovate in a fast-paced, ambiguous environment.

Compensation & Benefits

The role offers an annual salary with flexible compensation mix between salary and stock options along with comprehensive benefits including health plans, 401(k), stock options, paid time off, and more.

Inclusion

Netflix is an equal opportunity employer committed to diversity and inclusion. Accommodations are available during the hiring process if needed.

Key skills/competency

Analytics, SQL, Python, Scala, ETL, Tableau, D3, dashboards, data pipelines, machine learning

How to Get Hired at Netflix

🎯 Tips for Getting Hired

  • Research Netflix's culture: Study their mission, values, and recent news.
  • Customize your resume: Highlight SQL, Python, and analytics skills.
  • Prepare real examples: Detail experience with dashboards and automation.
  • Network on LinkedIn: Connect with current Netflix employees.

📝 Interview Preparation Advice

Technical Preparation

Practice writing complex SQL queries.
Review Python and Scala programming techniques.
Experiment with ETL and data warehousing tools.
Build dashboards using Tableau or similar tools.

Behavioral Questions

Describe a time you solved complex data problems.
Explain a project with minimal oversight.
Discuss handling ambiguous project requirements.
Share experience working cross-functionally with teams.