Pinterest - Staff Software Engineer, Batch Processing Platform
Requirements
• 6+ years of experience building and supporting large scalable big data infrastructure/platforms • Extensive experience with big data query technologies such as Spark SQL or Presto/Trino. • Proven skills in designing and maintaining scalable data infrastructure. • Proficiency in programming languages like Java, Scala, or Python. • Strong problem-solving abilities and a drive for innovation. • Excellent communication skills and the ability to collaborate across teams. • Bachelor's degree in a relevant field such as Computer Science or Engineering, or equivalent experience. • In-Office Requirement Statement: • We let the type of work you do guide the collaboration style. That means we're not always working in an office, but we continue to gather for key moments of collaboration and connection. • This role will need to be in the office for in-person collaboration 1-2 times/quarter, and therefore can be situated anywhere in the country. • Relocation Statement: • This position is not eligible for relocation assistance. Visit our PinFlex page to learn more about our working model. • #LI-REMOTE#LI-AH2 • At Pinterest we believe the workplace should be equitable, inclusive, and inspiring for every employee. In an effort to provide greater transparency, we are sharing the base salary range for this position. The position is also eligible for equity. Final salary is based on a number of factors including location, travel, relevant prior experience, or particular skills and expertise. • Information regarding the culture at Pinterest and benefits available for this position can be found here. • US based applicants only • $177,185—$364,795 USD • Our Commitment to Inclusion:
Responsibilities
• Design, implement, and optimize Pinterest’s batch processing infrastructure. • Collaborate with cross-functional teams to understand data needs. • Lead technical projects and initiatives, driving them to completion. • Mentor and guide junior engineers on best practices and architecture. • Ensure the reliability, scalability, and efficiency of data systems.
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