Описание
Accountabilities:
- Design, develop, and optimize scalable ETL/ELT data pipelines for batch and real-time processing using Python, SQL, PySpark, Spark Structured Streaming, and Delta Live Tables.
- Implement and maintain Medallion Architecture, organizing data into Bronze, Silver, and Gold layers to support data quality, performance, and analytical requirements.
- Process terabyte-scale datasets using distributed computing techniques, ensuring pipeline reliability, scalability, and efficient resource utilization.
- Manage Databricks environments, including workflows, clusters, Delta Lake, and related lakehouse components.
- Implement Unity Catalog for metadata management, granular access controls, OAuth-based authentication, and enterprise-level data security and governance.
- Develop and execute cloud migration strategies for legacy databases, including Oracle, Teradata, and SQL Server, to cloud-based data platforms and storage solutions such as Google Cloud Storage, Amazon S3, Azure Data Lake Storage, Snowflake, and Amazon Redshift.
- Build and maintain CI/CD pipelines using Terraform and GitHub Actions to automate infrastructure provisioning, data pipeline deployments, testing, and monitoring.
- Develop custom data processing scripts and data-driven APIs using Python and web frameworks such as Flask.
- Use Docker to containerize data processing workloads and support portable, consistent deployment environments.
- Evaluate emerging technologies and approaches for data-inten
Полное описание, компания и ссылка на вакансию — после входа.
ВойтиКонтакты работодателя (email/phone/telegram) скрыты из публичного превью —
отправьте резюме, чтобы мы связали вас напрямую.