Senior Cloud Data Engineer
CloudAI Technologies
About the role
Role Overview
As a Senior Cloud Data Engineer, you will be responsible for designing, building, and optimizing cloud-native data platforms that support enterprise-scale AI, analytics, and data-driven applications. You will work with AWS (required) and other cloud platforms, ensuring secure, efficient, and scalable data processing. This role requires strong experience in data engineering best practices, including ELT, data transformation, data cataloging, and governance.
You will collaborate closely with Data Scientists, AI/ML Engineers, and Cloud Architects to develop real-time, batch, and event-driven data pipelines that drive AI and business intelligence initiatives.
Responsibilities
- Cloud Data Engineering: Design and implement data architectures on AWS (required) and multi-cloud platforms (Azure, GCP preferred).
- ELT & Data Pipelines: Build and maintain scalable ELT pipelines using cloud-native tools such as AWS Glue, Lambda, Apache Airflow, and DBT.
- Data Ingestion & Processing: Develop batch and real-time data ingestion from structured and unstructured sources using Apache Spark, Kafka, and Kinesis.
- Data Governance & Security: Implement data governance frameworks, RBAC policies, data lineage tracking, and compliance with security standards.
- Data Cataloging & Discovery: Leverage tools like AWS Glue Data Catalog, Apache Atlas, and DataHub for metadata management and data discovery.
- Data Cleansing & Transformation: Ensure data quality, standardization, deduplication, and anomaly detection using modern data transformation frameworks.
- Multi-Cloud Data Solutions: Design cross-cloud data integration strategies, leveraging AWS, Azure, and GCP services.
- Performance Optimization: Optimize query performance, data partitioning, and indexing strategies for efficient analytics workloads.
- Collaboration & Leadership: Work with Data Scientists, Engineers, and Analysts to develop scalable AI and analytics-driven solutions.
- Infrastructure as Code (IaC): Automate data infrastructure provisioning using Terraform, AWS CloudFormation, or Pulumi.
Responsibilities
- Design and implement data architectures on AWS (required) and multi-cloud platforms (Azure, GCP preferred).
- Build and maintain scalable ELT pipelines using cloud-native tools such as AWS Glue, Lambda, Apache Airflow, and DBT.
- Develop batch and real-time data ingestion from structured and unstructured sources using Apache Spark, Kafka, and Kinesis.
- Implement data governance frameworks, RBAC policies, data lineage tracking, and compliance with security standards.
- Leverage tools like AWS Glue Data Catalog, Apache Atlas, and DataHub for metadata management and data discovery.
- Ensure data quality, standardization, deduplication, and anomaly detection using modern data transformation frameworks.
- Design cross-cloud data integration strategies, leveraging AWS, Azure, and GCP services.
- Optimize query performance, data partitioning, and indexing strategies for efficient analytics workloads.
- Work with Data Scientists, Engineers, and Analysts to develop scalable AI and analytics-driven solutions.
- Automate data infrastructure provisioning using Terraform, AWS CloudFormation, or Pulumi.
Skills
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