AWS Data Architect (Full - Time)

Envision; Alameda (CA); On-site; USD 170,000 - 210,000; Full time

Job summary

Envision in Alameda, CA is seeking a senior Data Architect/Lead to design enterprise-level data architectures on AWS. You will guide the end-to-end data platform from lakehouse design to BI governance and security, ensuring scalable solutions for analytics across business units.

You will mentor engineers, drive CI/CD adoption, and coordinate with Salesforce integration teams to optimize data flows and reporting, leveraging Tableau for insights.

Qualifications

• Design and define enterprise-level data architecture across AWS data platforms ensuring scalability, security, and performance.
• Architect, develop, and oversee implementation of data lakes/lakehouse solutions using AWS services such as S3, Glue, Redshift, Athena, EMR, Lambda, and Lake Formation.
• Lead design and development of robust ETL/ELT pipelines (batch and real-time) with high data quality and performance optimization.
• Integrate Salesforce with AWS data platforms to enable synchronized analytics.
• Define and implement enterprise data models including dimensional modeling (Star/Snowflake) for analytics and reporting.
• Establish BI architecture standards using Tableau with semantic layers, governance, and security.

Responsibilities

• Translate business requirements into scalable technical architecture and actionable data solutions.
• Provide hands-on leadership in SQL, Python/PySpark, query optimization, and performance tuning for large datasets.
• Define and enforce data governance, security (IAM, encryption, masking), and compliance standards across the data ecosystem.
• Guide teams in implementing CI/CD pipelines and Infrastructure as Code (Terraform/CloudFormation).
• Review designs and code for adherence to standards, scalability, and best practices.
• Collaborate with cross-functional teams to gather requirements and deliver scalable data solutions.
• Drive modernization initiatives including migration from legacy systems to AWS cloud environments.
• Ensure monitoring, logging, observability, backup, and disaster recovery are effectively implemented.
• Mentor data engineers and BI developers; establish development standards and reusable frameworks.
• Continuously evaluate emerging technologies and AWS innovations to optimize data capabilities.

Skills

AWS data architecture

Data modeling

Tableau

Salesforce integration

ETL/ELT design

CI/CD

Python/PySpark

Leadership / Architecture

Real-time & batch processing

BI governance

Tools

AWS Glue

Amazon Redshift

Amazon S3

Kinesis

Lake Formation

Terraform

CloudFormation

AWS Lake Formation

PySpark

Job description

1). 15+ 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 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 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 and ingestion and experience in CI/CD

Secondary Skills to be planned Post Hiring - Training Plan

Experience with real-time streaming tools (Kafka, Kinesis)

Knowledge of data governance and compliance standards

Prior experience in US-based client-facing roles

JD:

Design and define enterprise-level data architecture across AWS data platforms, ensuring scalability, security, performance, and alignment with business strategy.

Architect, develop, and oversee 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.

Collaborate with cross-functional teams to gather requirements and deliver scalable solutions

Drive modernization initiatives including migration from legacy/on-prem systems to AWS cloud environments.

Ensure monitoring, logging, observability, backup, and disaster recovery strategies are implemented effectively.

Mentor and technically guide data engineers and BI developers, establishing development standards and reusable frameworks.

Continuously evaluate emerging technologies, BI trends, and AWS innovations to recommend improvements and optimize enterprise data capabilities

Communication Skills:

Communicate effectively with internal and customer stakeholders

Communication approach: verbal, emails and instant messages

Interpersonal Skills:

Collaborative mindset; comfortable working across delivery, product, and sales functions

Ability to distill complex industry concepts into digestible insights for technical and non-technical audiences

Self-driven, organized, and adaptive in a fast-paced environment

Problem-Solving and Analytical Thinking:

Capability to troubleshoot and resolve issues efficiently.

Analytical mindset

Task/ Work Updates

Prior experience in working on Agile/Scrum projects with exposure to tools like Jira

Provides regular updates, proactive and due diligent to carry out responsibilities