Found Description
- Design and implement end-to-end data pipelines (ingestion → transformation → quality → delivery) for multiple use cases with growing autonomy
- Build and maintain data infrastructure components (streaming pipelines, transformations, APIs, catalogs) using modern data tools
- Implement robust data quality frameworks, monitoring, and alerting systems at scale
- Optimize data workflows for cost, performance, e and reliability across multiple datasets
- Mentor junior team members on data engineering best practices and tooling
- Have comprehensive MLOps expertise, advanced Python/SQL skills, and domain specialization;
enforce code quality standards across the team - Own key features and projects;
design and improve models, pipelines, and experimentation frameworks - Communicate and align with internal/external stakeholders;
turn business requirements into AI solutions;
make speed vs. quality trade-offs ...