Lead Spark Data Engineer

Fusemachines Fusemachines is a leading AI strategy, talent, and education services provider.

Founded by Sameer Maskey Ph.

D., Adjunct Associate Professor at Columbia University, Fusemachines has a core mission of democratizing AI.

With a presence in 4 countries (Nepal, the United States, Canada, and the Dominican Republic) and more than 450 full-time employees, Fusemachines brings global AI expertise to transform companies worldwide.

Founded in 2013, Fusemachines is a global provider of enterprise AI products and services, on a mission to democratize AI.

Leveraging proprietary AI Studio and AI Engines, the company helps drive the clients’ AI Enterprise Transformation, regardless of where they are in their Digital AI journeys.

With offices in North America, Asia, and Latin America, Fusemachines provides a suite of enterprise AI offerings and specialty services that allow organizations of any size to implement and scale AI.

Fusemachines serves companies in industries such as retail, manufacturing, and government.

Fusemachines continues to actively pursue the mission of democratizing AI for the masses by providing high-quality AI education in underserved communities and helping organizations achieve their full potential with AI.

Type: Full-time, Remote Job Description: We are looking for an experienced Lead Data Engineer to join our team to build the "Brain" of an IoT platform, a library that allows definition and metrics, validates it against a Virtual Schema, and generates optimized execution plans for both Spark (Batch) and Flink (Stream).

Qualification / Skill Set Requirement:

Responsibilities • Architect, design, and implement scalable and efficient data solutions on Spark and Flink.

Fusemachines is an Equal Opportunities Employer, committed to diversity and inclusion.

All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or any other characteristic protected by applicable federal, state, or local laws.