Position listed on behalf of a partner company managing applications and next steps.
Fully remote opportunity based in the United States.
Responsibilities
Architect, implement, and maintain secure, observable, and reliable data pipelines and services for analytics, machine learning, and product applications.
Develop efficient data models, storage strategies, and processing frameworks for high-volume, real-time, and batch workloads.
Drive technical design reviews, code reviews, and engineering best practices, providing mentorship to engineers.
Partner with product management, analytics, data science, and platform engineering teams to translate business objectives into production-grade data solutions and APIs.
Develop and maintain shared context-layer services, agent tools, authentication mechanisms, and APIs enabling AI-powered applications at scale.
Implement automated testing, monitoring, alerting, and incident response processes.
Evaluate and introduce modern big data technologies, development tools, and architectural patterns.
Document data flows, system interfaces, and operational runbooks.
Requirements
Bachelor's degree in Computer Science, Computer Engineering, or related technical field, or equivalent experience.
At least 8 years of software engineering experience with a focus on data-intensive applications or distributed systems.
Demonstrated experience designing, implementing, and supporting production-grade data pipelines and platforms using modern big data, streaming, and/or cloud-native technologies.
Strong understanding of scalable data processing, data modeling, storage strategies, distributed computing, system reliability, and performance optimization.
Experience with automated testing, monitoring, observability, alerting, and operational troubleshooting.
Hands-on experience building agentic solutions or AI-powered capabilities; experience with Databricks is an advantage.
Ability to build robust services, integrations, authentication mechanisms, and APIs.
Proven ability to work effectively with product, analytics, and data science teams.