Monitor and optimize data pipelines, workflows, and automated routines in a production environment;
Analyze logs, metrics, and events for root cause investigations, applying advanced analysis techniques and, when possible, predictive models;
Develop and enhance automation mechanisms for error remediation, reprocessing, and pipeline recovery;
Implement continuous improvements to pipelines with a focus on efficiency, scalability, and reliability;
Support Machine Learning and AI initiatives, including data preparation, model monitoring, and maintaining inference pipelines;
Document technical procedures, incidents, and automated playbooks.

Experience with production support, technical support, or incident response (SRE/DataOps is a plus);
Knowledge of process automation (scripting, orchestration, CI/CD);
Experience with Python and SQL;
Familiarity with Machine Learning concepts, model data pipelines, or MLOps;
Ability to work autonomously, with analytical thinking and decision-making under pressure.

Data Engineer – AWS (Mid-level)

Key skills