Top Data Engineer Jobs in Seattle, WA

Design and build data pipelines for foundational data sets related to Identity, Account, Profile and Device data. Collaborate with cross-functional teams to define technical requirements, implement ETL strategies, automate data quality checks, and support downstream teams. Work on cloud-based technology to achieve scalability, reliability, and performance in handling extensive amounts of data.

Snap Inc. is seeking a Machine Learning Engineer to create models driving value for users, advertisers, and the company. Responsibilities include evaluating technical tradeoffs, performing code reviews, and building robust products. The engineer should have a strong understanding of machine learning algorithms and frameworks, prioritize duties, and collaborate effectively with internal and external partners.

Develop and maintain sophisticated analytics solutions, automate reporting, uncover insights, advise strategic business decisions, analyze complex data sets, collaborate with multi-functional teams, ensure data quality, communicate findings and recommendations.

Lead a team of Machine Learning Engineers and Data Scientists to build machine learning solutions for fraud prevention in the financial services industry. Partner with ML ops and ML platform teams to improve research efficiency and model quality. Define frameworks for evaluating model performance and accelerate the model development processes. Evaluate and introduce new algorithms and model architectures to improve performance.

Senior/Staff Machine Learning Engineer at Rokt, responsible for designing, building, and productionizing machine learning models to solve business challenges. Collaborate with product managers and engineers, maintain code quality, research and prototype new models, and mentor team members.

Building, orchestrating, and monitoring model pipelines, scaling machine learning algorithms, enhancing ML Engineering platforms, implementing ML Ops, writing production-ready code, collaborating with teams, and staying updated on the latest technologies.

Monitoring, maintaining, and supporting AWS data platform infrastructure, critical data pipelines, and enterprise dashboards within defined SLAs. Collaborating within cross-functional teams and resolving issues with AWS hosted data platforms.