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Netflix

Data Engineer (L5) - Ads

Reposted 22 Days Ago
Remote
Hiring Remotely in USA
380K-610K Annually
Senior level
Remote
Hiring Remotely in USA
380K-610K Annually
Senior level
Design, build, and own large-scale batch and streaming data pipelines and core Ads data products. Collaborate with analytics, data science, and engineering teams to model, transform, govern, and deliver high-quality ad datasets for analysis, ML, and reporting while ensuring privacy compliance.
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At Netflix, our mission is to entertain the world. Together, we are writing the next episode - pushing the boundaries of storytelling, global fandom and making the unimaginable a reality. We are a dream team obsessed with the uncomfortable excitement of discovering what happens when you merge creativity, intuition and cutting-edge technology. Come be a part of what’s next.

About the team

Ads Data Engineering team sits at the core of building a data ecosystem that will power Netflix’ understanding and decision making about what impact ads have on our business. This team’s main focus is to build rich, connected, and easily accessible data products about ad inventories, forecasting, targeting, ad serving, pacing and much more. We are looking for passionale, mature, and curious software engineers with strong data intuition, analytical mindset and ad ecosystem experience, to contribute to the team’s impact in a quickly evolving.

We are hiring for several different roles, ranging from senior to staff level for this team.

Who are you?

  • Beyond talented, you are curious, creative, and tenacious. 

  • Sharp communicator who can break down and explain complex data problems in clear and concise language. 

  • You have an extensive background and strong technical expertise working with data at scale, experience with advertising data (preferred) and understand how to build for privacy, and business impact

  • You have a high tolerance for ambiguity and fast-changing context

  • Hands-on experience building batch or streaming production data pipelines, ideally using one or more distributed processing frameworks such as Spark, Flink or Hive/Hadoop

  • Knowledge in data modeling and establishing data architecture across multiple systems

  • Thrive in a fast paced environment, and see yourself as a partner with the business with the shared goal of moving the business forward.

  • Create code that is understandable, simple, and clean, and take pride in its beauty.

  • Love freedom and hate being micromanaged. Given context, you're capable of self-direction.

  • Passionate about data quality and delivering effective data to impact the business.  

What will you do?

  • Architect, strengthen, and expand the core data products  that scale our Ads business.  You’ll get on a team of talented data engineers and envision how all the data elements from multiple sources should fit together as a whole, and then execute on that plan.

  • Fully own critical portions of Netflix' Ads data products. Collaborate with stakeholders to understand needs, model tables using software engineering and data warehouse best practices, and develop large scale data processing solutions to ensure the timely delivery of high quality data.

  • Partner with Analytics Engineers, Data Scientists, and Software Engineers to create data products that will serve analysis, ML and reporting needs intuitively

  • Develop best practices for governance of data sets with sensitive information

  • Build strong and collaborative partnerships with data scientists, analytics engineers, and Machine Learning practitioners

What (ideally) do you know? 

  • Domains related to advertising, data privacy, GDPR

  • Data warehousing, data modeling, and data transformation for both batch and streaming  

  • Hands on command programming languages such as python, scala or java as well data exploration using sql

  • Expert at building performant data pipelines and optimizing existing workflows for new features

  • Big Data tech - Hadoop, Spark, Flink, Stream processing, Hive, Presto, etc.  

  • Experience with sourcing and modeling data from application APIs

  • Team based software development tools and best practices


Generally, our compensation structure consists solely of an annual salary; we do not have bonuses. You choose each year how much of your compensation you want in salary versus stock options. To determine your personal top of market compensation, we rely on market indicators and consider your specific job family, background, skills, and experience to determine your compensation in the market range. The range for this role is $380,000.00 - $610,000.00. This compensation range will vary based on location.

Netflix provides comprehensive benefits including Health Plans, Mental Health support, a 401(k) Retirement Plan with employer match, Stock Option Program, Disability Programs, Health Savings and Flexible Spending Accounts, Family-forming benefits, and Life and Serious Injury Benefits. We also offer paid leave of absence programs. Full-time hourly employees accrue 35 days annually for paid time off to be used for vacation, holidays, and sick paid time off. Full-time salaried employees are immediately entitled to flexible time off. See more details about our Benefits here.

Netflix is a unique culture and environment. Learn more here.

Inclusion is a Netflix value and we strive to host a meaningful interview experience for all candidates. If you want an accommodation/adjustment for a disability or any other reason during the hiring process, please send a request to your recruiting partner.

We are an equal-opportunity employer and celebrate diversity, recognizing that diversity builds stronger teams. We approach diversity and inclusion seriously and thoughtfully. We do not discriminate on the basis of race, religion, color, ancestry, national origin, caste, sex, sexual orientation, gender, gender identity or expression, age, disability, medical condition, pregnancy, genetic makeup, marital status, or military service.

Job is open for no less than 7 days and will be removed when the position is filled.

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