AWS Data Architect

Job Title: AWS Data Architect with Salesforce cloud

Last round Face to face, Alameda or Santa Clara

Primary (Must-Have) Skills

1. 13+ years of overall IT experience, with 8+ years in Data Engineering & Analytics, including end-to-end data solution design, architecture, and enterprise data platform implementation.
2. 6+ years of hands-on experience in AWS data architecture, including Amazon S3, AWS Glue, Redshift, Athena, Lambda, EMR, Lake Formation, Kinesis, and RDS with a strong understanding of cloud-native data patterns & SQL development and advanced data modeling.
3. 3+ years of experience in integrating Salesforce (Sales Cloud / Service Cloud) with external data platforms using APIs, connectors, and middleware tools.
4. 3+ years of experience in leading data engineering teams as a Technical Architect / Lead, providing architecture guidance, code reviews, design standards, and mentoring developers.
5. 4+ years of hands-on experience with Tableau, including dashboard development, data modeling, performance tuning, and data visualization best practices.
6. 3+ years of experience in real-time and batch data processing architectures, including streaming frameworks and large-scale data ingestion, alongside experience in CI/CD.

Job Description & Responsibilities (RNR)

• Design and define enterprise-level data architecture across AWS data platforms, ensuring scalability, security, performance, and alignment with business strategy.
• Architect, develop, and oversee the implementation of data lakes/lakehouse solutions using AWS services such as S3, Glue, Redshift, Athena, EMR, Lambda, and Lake Formation.
• Lead the design and development of robust ETL/ELT pipelines (batch and real-time), ensuring high data quality, reliability, and performance optimization.
• Integrate and synchronize data between Salesforce and AWS data platforms.
• Define and implement enterprise data models (conceptual, logical, physical), including dimensional modeling (Star/Snowflake schemas) to support analytics and reporting needs.
• Establish BI architecture standards using Tableau, including semantic layer design, dashboard governance, security (row-level security), and performance optimization.
• Translate complex business requirements into scalable technical architecture and actionable data solutions.
• Provide hands-on technical leadership in SQL, Python/PySpark, query optimization, and performance tuning for large-scale datasets.
• Define and enforce data governance, security frameworks (IAM, encryption, masking), and compliance standards across the data ecosystem.
• Guide teams in implementing CI/CD pipelines, Infrastructure as Code (Terraform/CloudFormation), and DevOps best practices for data platforms.
• Review solution designs and code to ensure adherence to architectural standards, scalability principles, and best practices. [1]