Data Engineer: Scalable Pipelines for ML Workflows

Roles and Responsibility - Design, build, and maintain scalable and reliable data pipelines for dataset creation, transformation, and benchmarking Own and optimize Airflow pipelines on AWS for data processing, orchestration, and evaluation workflows Write efficient, production-grade SQL and Python code for large-scale data processing and analysis Partner closely with ML engineers to enable model training, evaluation, and benchmarking pipelines Improve pipeline performance, reliability, and observability, ensuring high data quality in production Build and maintain systems to support model performance tracking and data drift monitoring Troubleshoot and resolve data issues across pipelines, ensuring minimal impact on ML workflows Contribute to data architecture decisions and best practices across the platform Collaborate cross-functionally with ML, platform, and data teams to support scalable ML infrastructure What Were Looking For 35 years of experience in Data Engineering, Data Platforms, or related roles Strong proficiency in Python and SQL with experience in production systems Hands-on experience with AWS services (S3, EC2, SageMaker or similar) Solid experience building and managing Airflow (or similar orchestration tools) Strong understanding of data engineering fundamentals (ETL/ELT, data modeling, pipeline design) Experience working with large-scale datasets and distributed data systems Experience supporting ML workflows, datasets, or evaluation pipelines Strong problem-solving skills and ability to work independently in a fast-paced environment Nice to Have Experience with ML infrastructure, MLOps, or model evaluation workflows Exposure to biometric systems or computer vision datasets Familiarity with data quality frameworks, monitoring, and observability tools Experience working in SaaS or high-scale production environments #J-18808-Ljbffr New Groyp Talentoj