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Recognizable employer and metro location increase applicants, but niche Snowflake+MLOps and seniority limit broad competition.
Strong Snowflake, AWS and MLOps requirements create medium transferability across industries.
Explicit 8+ years, mandatory AWS, Snowflake, MLOps skills and onsite requirement enforce high strictness.
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Architect and maintain backend microservices for deploying and scaling machine learning models in production with real-time prediction capabilities.
Design and optimize batch and real-time data pipelines and analytical workflows using AWS and Snowflake for high-performance, low-latency insights.
Lead technical execution including coding, code quality standards, and mentorship of junior engineers within the data analytics software team.
8+ years of professional software development experience focused on data-intensive or ML backend systems.
Hands-on expertise in AWS ecosystem (SageMaker, Lambda, ECS/EKS, Step Functions) and Snowflake (advanced SQL, data modeling, performance tuning).
Proficient in programming languages such as Python for backend systems, REST/gRPC services, and data pipelines.
Must be willing to work onsite 5 days a week. Relocation assistance available.
Experienced in end-to-end ML infrastructure productionization including MLOps CI/CD practices, model monitoring, and drift tracking.
Skilled in collaborating across enterprise architecture, product, data science, and engineering teams to deliver enterprise-grade, secure, scalable cloud solutions.
Effective technical leader who balances hands-on coding with mentoring engineers and enforcing high-quality software engineering standards.