About Pinterest:
Millions of people around the world come to our platform to find creative ideas, dream about new possibilities and plan for memories that will last a lifetime. At Pinterest, we’re on a mission to bring everyone the inspiration to create a life they love, and that starts with the people behind the product.
Discover a career where you ignite innovation for millions, transform passion into growth opportunities, celebrate each other’s unique experiences and embrace the flexibility to do your best work. Creating a career you love? It’s Possible.
At Pinterest, AI isn't just a feature, it's a powerful partner that augments our creativity and amplifies our impact, and we’re looking for candidates who are excited to be a part of that. To get a complete picture of your experience and abilities, we’ll explore your foundational skills and how you collaborate with AI.
Through our interview process, what matters most is that you can always explain your approach, showing us not just what you know, but how you think. You can read more about our AI interview philosophy and how we use AI in our recruiting process here.
About tvScientific
tvScientific is the first and only CTV advertising platform purpose-built for performance marketers. We leverage massive data and cutting-edge science to automate and optimize TV advertising to drive business outcomes. Our solution combines media buying, optimization, measurement, and attribution in one, efficient platform. Our platform is built by industry leaders with a long history in programmatic advertising, digital media, and ad verification who have now purpose-built a CTV performance platform advertisers can trust to grow their business.
As a Data Engineer at tvScientific, you will be a key player in implementing the robust data infrastructure to power our data-heavy company. You will collaborate with our cross-functional teams to evolve our core data pipelines, design for efficiency as we scale, and store data in optimal engines and formats. This is an individual contributor role, where you will work to define and implement a strategic vision for data engineering within the organization.
What you'll do:
- Design and implement robust data infrastructure in AWS, using Spark with Scala
- Evolve our core data pipelines to efficiently scale for our massive growth
- Store data in optimal engines and formats, matching your designs to our performance needs and cost factors
- Collaborate with our cross-functional teams to design data solutions that meet business needs
- Design and implement knowledge graphs, exposing their functionality both via Batch Processing and APIs
- Leverage and optimize AWS resources while designing for scale
- Collaborate closely with our Data Science and Product teams
- How we'll define success:
- Successful design and implementation of scalable and efficient data infrastructure
- Timely delivery and optimization of data assets and APIs
- High attention to detail in implementation of automated data quality checks
- Effective collaboration with cross-functional teams
What we're looking for:
- Production data engineering experience
- Proficiency in Spark and Scala, with proven experience building data infrastructure in Spark using Scala is preferred
- Experience in delivering significant technical initiatives and building reliable, large scale services
- Experience in delivering APIs backed by relationship-heavy datasets
- Familiarity with data lakes, cloud warehouses, and storage formats
- Strong proficiency in AWS services
- Expertise in SQL for data manipulation and extraction
- Excellent written and verbal communication skills
- Bachelor's degree in Computer Science or a related field
- Demonstrated ability to use AI to improve speed and quality in your day-to-day workflow for relevant outputs
- Strong track record of critical evaluation and verification of AI-assisted work (e.g., testing, source-checking, data validation, peer review)
- High integrity and ownership: you protect sensitive data, avoid over-reliance on AI, and remain accountable for final decisions and deliverables
- Nice-to-haves:
- Experience in adtech
- Experience implementing data governance practices, including data quality, metadata management, and access controls
- Strong understanding of privacy-by-design principles and handling of sensitive or regulated data
- Familiarity with data table formats like Apache Iceberg, Delta
- Previous experience building out a Data Engineering function
- Proven experience working closely with Data Science teams on machine learning pipelines
In-Office Requirement Statement:
- We recognize that the ideal environment for work is situational and may differ across departments. What this looks like day-to-day can vary based on the needs of each organization or role.
Relocation Statement:
- This position is not eligible for relocation assistance. Visit our PinFlex page to learn more about our working model.
Our Commitment to Inclusion:
Pinterest is an equal opportunity employer and makes employment decisions on the basis of merit. We want to have the best qualified people in every job. All qualified applicants will receive consideration for employment without regard to race, color, ancestry, national origin, religion or religious creed, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, age, marital status, status as a protected veteran, physical or mental disability, medical condition, genetic information or characteristics (or those of a family member) or any other consideration made unlawful by applicable federal, state or local laws. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you require a medical or religious accommodation during the job application process, please complete
this form for support.
By submitting this application, I certify that all information submitted in my application and throughout the hiring process is true, accurate, and complete to the best of my knowledge. I understand that any false statement, omission, or misrepresentation may disqualify me from employment consideration or result in termination if discovered after hire.