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Lead the evolution of the recommendations engine system into a scalable distributed architecture, ensuring production stability and performance while mentoring engineers and collaborating with ML teams.

Own, build, and optimize Spark-based production data pipelines and ML data lake on GCP Dataproc to support recommender systems. Collaborate with ML engineers, resolve performance bottlenecks, and deliver scalable, reliable data products for AI-driven personalization.

The Senior Data Engineer will design and maintain streaming data pipelines, develop analytical databases, and ensure data system reliability while collaborating with teams across the organization.

The Lead Backend Developer will shape architectural direction, mentor engineers, design systems for behavioral triggers, and ensure operational excellence in processing high-volume event data.

The Product Security Engineer will implement security testing, manage vulnerabilities in codebases, and integrate security practices into CI/CD pipelines while collaborating with engineering teams.

Design and build scalable data systems and pipelines, migrate legacy systems, ensure data governance, and collaborate with cross-functional teams to enhance data accessibility and reliability.

Design and implement distributed software systems to handle data at scale. Perform code reviews and collaborate with teams. Monitor and manage business-critical services.

The role involves designing and maintaining scalable backend systems, building data pipelines, creating an intuitive reporting UI, and mentoring junior engineers in a collaborative environment.