Software Engineer II, Big Data, tvScientific
About this role
tvScientific is seeking an experienced Software Engineer II to design and implement a robust data infrastructure within a performance-driven CTV advertising platform. The ideal candidate will collaborate with cross-functional teams, evolve data pipelines, and utilize AWS technologies to optimize and scale data solutions.
What you'll do
- Design and implement data infrastructure in AWS using Spark with Scala
- Evolve core data pipelines for scaling
- Store data in optimal engines and formats
- Collaborate with cross-functional teams for data solutions
- Design knowledge graphs accessible via Batch Processing and APIs
- Leverage AWS resources for optimal performance
What they're looking for
- Production data engineering experience
- Proficiency in Spark and Scala
- Experience with APIs and large-scale services
- Familiarity with data lakes and cloud storage
- Strong knowledge of AWS services
- Expertise in SQL for data manipulation
- Excellent communication skills
- Ability to use AI for workflow improvements
Benefits
- Flexible work options
- Opportunity for career growth
- Collaborative work environment
- Access to experienced team leaders
- Focus on innovation and creativity
- Commitment to data integrity and privacy
Opens the official application on the employer’s site. No login required.
Pinterest builds a large-scale platform serving millions of users, with infrastructure spanning security, database systems, and mobile products, supported by data systems and advertising technology. The company is hiring Software Engineers II and experienced engineers across security, infrastructure, iOS development, and data engineering to enhance platform capabilities, improve detection and response systems, optimize performance, and leverage AI-driven solutions.
- Website
- pinterest.com
Likely interview questions
- Walk us through a production data pipeline you designed using Spark and Scala. How did you handle scalability and what optimizations did you implement?
- Describe your experience building APIs backed by relationship-heavy datasets. What challenges did you face and how did you solve them?