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AWS Data Infra Architect
1 month ago
Role: AWS Data Infra Architect
Location: onsite to Alameda
Duration: FTE
Conditions: Strictly Onsite and Locals Only
Key Responsibilities
- Experienced to provide infrastructure solution for data pipelines, data warehousing and ETL/ELT solutions on AWS
- Familiarity of working with other data integration tools (e.g., Tableau, , Dbt cloud etc.,)
- Understand and review data architecture frameworks including data models, data flow diagrams, and metadata management.
- Understand and review scalable and efficient data storage solutions, including relational databases, data warehouses, and data lakes.
- Work with data engineers and developers to ensure the successful implementation of data solutions and troubleshoot issues as they arise.
- Ensure data architecture and solutions comply with industry standards and regulations, including GDPR, HIPAA, and other relevant frameworks.
- Define configurations at AWS services to maintain data security measures to protect sensitive information.
- Knowledge of AWS cloud security best practices and tools in Data Encryption, Access Management, Network Security, Security Policies and compliance
Skills
Experience in setting up Infra for data engineering platform on AWS with focus on data security and data governance. Which other data engineering can re-use as a framework. Skills which include but not limited to -
- Setup and manage access to data using AWS Lake formation
- Using AWS Athena to leverage IAM roles for security and integrates with AWS Lake Formation
- Use RDS / AWS Aurora with encryption, automated backups, and fine-grained access controls
- Use of AWS Glue w.r.t data cataloguing, data crawling across different sources and
- Data warehouse solutions like Redshift integration with AWS IAM
- Understands data lake architecture implementation using S3 AWS
Ability to review, debug and provide solutions to Data warehousing solutions on AWS (e.g., Amazon Redshift,). Sills which include but not limited to -
- Data quality and transformation issues
- Performance bottlenecks
- ETL process failures
- Data skew issues
