> Markdown version of [/jobs/ext/2709329-software-engineer-ii](https://www.wearedevelopers.com/jobs/ext/2709329-software-engineer-ii). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Software Engineer II - **Company:** Pinterest - **Location:** San Francisco, CA, United States (Remote available) - **Salary:** $123,696.0 - $254,667.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Apache HTTP Server, Batch Processing, Data Validation, Information Engineering, Data Governance, Data Infrastructure, Data Stores, Data Systems, Graph Database, Meta-Data Management, Operational Databases, SQL Databases, Data Processing, Apache Spark, Data Lakes, Core Data, Information Technology, Machine Learning Operations - **Published:** September 4, 2026 - **Apply:** https://startup.jobs/software-engineer-ii-big-data-tvscientific-pinterest-8000038 ## About the Role * 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 ## Description 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 ## Related Videos - [Empowering Retail Through Applied Machine Learning](https://www.wearedevelopers.com/videos/976-empowering-retail-through-applied-machine-learning) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Fault Tolerance and Consistency at Scale: Harnessing the Power of Distributed SQL Databases](https://www.wearedevelopers.com/videos/1520-fault-tolerance-and-consistency-at-scale-harnessing-the-power-of-distributed-sql-databases) - [Alibaba Big Data and Machine Learning Technology](https://www.wearedevelopers.com/videos/37-alibaba-big-data-and-machine-learning-technology) - [Building Multi-Tenant ASP.NET Core Applications: Best Practices and Real-World Solutions](https://www.wearedevelopers.com/videos/1552-building-multi-tenant-asp-net-core-applications-best-practices-and-real-world-solutions) - [The Data Mesh as the end of the Datalake as we know it](https://www.wearedevelopers.com/videos/156-the-data-mesh-as-the-end-of-the-datalake-as-we-know-it) ## Related Articles - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [Dev Digest 120 - Apple and peers](https://www.wearedevelopers.com/magazine/455-dev-digest-120-apple-and-peers) - [Dev Digest 137 - AI'm not sure about this](https://www.wearedevelopers.com/magazine/485-dev-digest-137-ai-m-not-sure-about-this) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production)