Software Engineer II, Big Data, tvScientific

Summary

Pinterest is a platform that helps people discover creative ideas and plan meaningful experiences, and tvScientific provides a CTV advertising platform for performance marketers. The Software Engineer II will build and scale data infrastructure, pipelines, knowledge graphs, and APIs using AWS, Spark, and Scala while collaborating with Data Science, Product, and other cross-functional teams.

Responsibilities

• 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
• 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

Skills

• 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
• US based applicants only
• 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

Qualifications

Must Haves

• 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
• US based applicants only

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

Benefits

• The position is also eligible for equity.