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Mid-level data engineer title, metro location, and broad skillset increase applicant competition.
Core data engineering skills transfer across industries, though MLOps emphasis raises some specialization.
Explicit 5–8 years plus many mandatory technical skills makes screening highly selective.
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Design, build, and maintain scalable batch and real-time data pipelines and robust ETL/ELT frameworks.
Implement MLOps frameworks (e.g., MLFlow, Kubeflow, SageMaker) to operationalize machine learning models, including model training, deployment automation, and monitoring.
Develop and maintain CI/CD pipelines and containerized deployments using Docker and Kubernetes; ensure data quality, governance, and performance optimization across data platforms.
5-8 years professional experience in Data Engineering, Data Platform Engineering, or Data & AI solution development.
Expert-level Python programming skills and strong SQL development and database design experience.
Experience with ML lifecycle management tools such as MLFlow (preferred), Kubeflow, or SageMaker.
Bachelor's or Master's degree in Computer Science, IT, Data Engineering, Software Engineering, or a related field.
Experienced in end-to-end data engineering with a strong focus on scalable data pipeline architecture and ETL/ELT production systems.
Proficient in MLOps frameworks and lifecycle management, capable of partnering with Data Scientists to operationalize ML models.
Hands-on with cloud DevOps tools and container orchestration (Docker, Kubernetes), with a strong grasp of software design patterns and microservice/event-driven architectures.