ARM (Advanced RISC Machine), Amazon Simple Storage Service (S3), Amazon Web Services (AWS), Architectural Services, Artificial Intelligence (AI), Best Practices, Business Intelligence, Capacity Management, Cataloguing, Cloud Architecture, Cloud Computing, Cryptography, Data Processing, Data Storage, DataArchitect Data Modeling Tool, Database Extract Transform and Load (ETL), Ecosystems, GCP (Good Clinical Practices), HIPAA (Health Insurance Portability and Accountability Act), Healthcare, Information/Data Security (InfoSec), Looker, Metadata, Microsoft Windows Azure, NoSQL, PCI-DSS, Power BI, Python Programming/Scripting Language, Requirements Management, SQL (Structured Query Language), Scalable System Development, Security Infrastructure, Tableau, Thought Leadership
Job Title: Cloud Data Architect
Duration- Fulltime Permanent
Location: Westlake Village, CA / Carrolton, TX Onsite
Job Description:
Must Have Technical/Functional Skills
• 8+ years in data engineering/architecture; 3+ years in cloud-native architectures.
• Hands-on expertise in at least one major cloud:
o AWS (S3, Redshift, EMR, Glue, Athena, Lake Formation)
o Azure (Synapse, Data Lake, ADF, Databricks)
o GCP (BigQuery, Dataflow, Dataproc)
• Strong experience with:
o Distributed data processing (Spark, Databricks)
o SQL/NoSQL design
o Event streaming (Kafka, Kinesis, Pub/Sub)
o Data modeling (Star/Snowflake, Data Vault)
• Proficiency in Python, PySpark, SQL.
• Knowledge of data governance, security, metadata management.
Preferred Qualifications
• Cloud Architect certifications (AWS Solutions Architect, Azure Architect Expert, GCP Professional Data Engineer).
• Experience with BI/visualization tools (Power BI, Tableau, Looker).
• Familiarity with ML/AI platforms (Azure ML, SageMaker, Vertex AI).
• Experience in regulated industries (BFSI, healthcare).
Key Responsibilities
1. Architecture & Design
• Design end to end cloud data architectures, including ingestion, processing, storage, governance, and consumption layers.
• Define modern data platform patterns such as lakehouse, data mesh, and real time streaming architectures.
• Select appropriate cloud-native services (e.g., AWS Redshift/S3/Glue, Azure Synapse/ADF/Databricks, GCP BigQuery/Dataflow).
2. Data Engineering & Integration
• Architect pipelines for batch and real time data ingestion.
• Enable scalable data processing using Spark, Databricks, Kafka, Flink, or cloud-native tools.
• Define ETL/ELT frameworks and best practices.
3. Cloud Platform Expertise
• Build secure and scalable cloud data ecosystems.
• Optimize data storage, compute performance, cost, and operational efficiency.
• Implement Infrastructure as Code (Terraform, ARM, CloudFormation).
4. Data Governance & Security
• Develop data cataloging, lineage, classification, and DQ frameworks.
• Implement cloud IAM, role-based access, encryption, and compliance (GDPR, HIPAA, SOC2, PCI-DSS if BFSI).
5. Stakeholder Collaboration
• Work with business stakeholders to translate requirements into technical architectures.
• Partner with security, platform engineering, and product teams.
• Provide thought leadership in cloud data strategy and modernization.
6. Documentation & Best Practices
• Create reference architectures, standards, and reusable frameworks.
• Conduct architectural reviews and capacity planning.