
Data Engineer
2 days ago
Design, implement, and maintain data pipelines for processing large datasets, ensuring data availability, quality, and efficiency for machine learning model training and inference.
Collaborate with data scientists to streamline the deployment of machine learning models, ensuring scalability, performance, and reliability in production environments.
- Develop and optimize ETL (Extract, Transform, Load) processes, ensuring data flow from various sources into structured data storage systems.
- Automate ML workflows using ML Ops tools and frameworks (e.g., Kubeflow, MLflow, TensorFlow Extended (TFX)).
- Ensure effective model monitoring, versioning, and logging to track performance and metrics in a production setting.
- Collaborate with cross-functional teams to improve data architectures and facilitate the continuous integration and deployment of ML models.
- Work on data storage solutions, including databases, data lakes, and cloud-based storage systems (e.g., AWS, GCP, Azure).
- Ensure data security, integrity, and compliance with data governance policies.
- Perform troubleshooting and root cause analysis on production-level machine learning systems.
- Skills: Glue, Pyspark, AWS Services, Strong in SQL; Nice to have : Redshift, Knowledge of SAS Dataset
*Mandatory Competencies*
DevOps/Configuration Mgmt - DevOps/Configuration Mgmt - Docker
ETL - ETL - AWS Glue
DevOps/Configuration Mgmt - Cloud Platforms - AWS
DevOps/Configuration Mgmt - DevOps/Configuration Mgmt - Containerization (Docker, Kubernetes)
Database - Sql Server - SQL Packages
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