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# AWS Data Architect

- **Company:** Quantum Technologies
- **Location:** Virginia City, NV, United States
- **Experience:** Expert
- **Salary:** $120,000.0
- **Contract:** Temporary contract
- **Skills:** Query Performance, Artificial Intelligence, Airflow, Amazon Web Services, Amazon S3, Data Analysis, Big Data, Information Systems, Data Architecture, Information Engineering, Data Governance, Extract Transform Load (ETL), Data Transformation, Data Warehousing, Distributed Data Store, Amazon DynamoDB, Apache Hadoop, Identity and Access Management, Python (Programming Language), Metadata, Power BI, SQL Databases, Data Streaming, Tableau (Software), Data Storage Technologies, Snowflake, Apache Spark, Data Layers, Data Lakes, Pyspark, Information Technology, AWS Glue, Data Analytics, AWS Data Analytics, Apache Kafka, Data Management, Tools for Reporting, Virtual Agents, Api Design, Stream Processing, Data Pipelines, Amazon Redshift, Microservices
- **Published:** September 28, 2026
- **Apply:** https://www.dice.com/job-detail/5da2bc80-56f6-46b6-9e23-9d133f7c5fdc

## About the Role

* Minimum 15+ years of overall experience - must
 * At least 6 years of hands-on AWS Data Architecture experience - must
 * Minimum 9 years of Data Engineering experience - must
 * Excellent communication skills - CTO level interactions, We are seeking a highly experienced and hands-on AWS Data Architect to lead the design, implementation, and governance of enterprise-scale data platforms on AWS. This role requires deep technical expertise, strong architectural ownership, and the ability to actively contribute to development while guiding teams.
The ideal candidate will be a player-coach capable of defining architecture, building solutions, and ensuring best practices across data engineering, analytics, and governance., Overall 15+ years of experience, including 5 to 7 years in AWS Data Architecture.
Core AWS Expertise

 * Deep experience with:

 * S3 (data lake design)
 * AWS Glue (ETL, catalog)
 * Amazon Redshift (data warehouse design & optimization)
 * Lambda, Step Functions (orchestration)
 * IAM, Lake Formation (security)

Data Engineering & Processing

 * Strong hands-on experience with:

 * PySpark / Spark (EMR or Glue)
 * SQL (advanced level)
 * Python for data pipelines

Experience with streaming (Kinesis / Kafka) is a plus

Data Architecture

 * Expertise in:

 * Data lake / lakehouse architectures
 * Data modeling (dimensional + normalized)
 * Metadata and cataloging strategies
 * Handling large-scale, distributed data systems

Modern Data Stack (Preferred)

 * Exposure to:

 * dbt, Airflow, Snowflake (optional but valuable)
 * BI tools (QuickSight, Tableau, Power BI)
 * API-based ingestion and microservices-based data flows
 * Amazon Quick Suite (QuickSight, Quick Chat, Quick Flows, Quick Automate, Quick Research)
 * SQL & Data Modeling
 * AWS Analytics Stack
 * Dashboard Design
 * AI Agent Design & Configuration
 * Workflow Automation & Business Process Optimization

Soft Skills:

 * Strong ownership mindset and ability to drive architecture end-to-end
 * Excellent communication with both technical and business stakeholders
 * Ability to work in fast-paced, ambiguous environments
 * Proven leadership and mentoring experience

Nice-to-Have:

 * AWS Certifications (Solutions Architect, Data Analytics Specialty)
 * Experience with data governance frameworks / regulatory compliance
 * Background in large enterprise transformations

Education:

Bachelor s degree in Computer Science, Engineering, Information Systems, or equivalent experience

## Description

Architecture & Design

 * Define and own end-to-end data architecture on AWS (ingestion, storage, transformation, consumption)
 * Design scalable, secure, and high-performing data platforms (lakehouse / modern data stack)
 * Establish standards for data modeling, partitioning, metadata, and lifecycle management
 * Architect solutions for both batch and real-time data processing

Hands-On Engineering

 * Build and implement pipelines using AWS Glue, EMR, Lambda, Step Functions
 * Design data storage using S3, Redshift, RDS, DynamoDB
 * Develop and optimize ETL/ELT pipelines using PySpark, SQL, and Python
 * Implement data transformation frameworks and reusable components

Data Governance & Security

 * Define and enforce data governance, cataloging, and lineage
 * Design row-level security, IAM policies, encryption strategies
 * Work with AWS Lake Formation / Glue Data Catalog

Performance & Optimization

 * Optimize data pipelines for performance and cost efficiency
 * Drive SPICE/BI dataset optimization (if QuickSight or similar tools involved)
 * Improve query performance in Redshift/S3-based architectures

Collaboration & Leadership

 * Work closely with business, analytics, and engineering teams
 * Lead technical discussions and design reviews
 * Mentor data engineers and enforce engineering best practices
 * Act as the primary owner of data architecture decisions

Migration & Modernization

 * Lead legacy data platform migrations (e.g., on-prem, Tableau, Hadoop) to AWS
 * Define strategies for data platform modernization and cloud-native adoption
 * Support large-scale BI/reporting migrations (e.g., to QuickSight)

Reporting Frameworks & Reusable Components

 * Create reusable reporting templates, dataset templates, and QuickSight themes.
 * Build standardized KPIs, calculated fields, and metric definitions.
 * Design modular AI agents and workflow templates that can be used across multiple business functions.
 * Design modular reporting components that can be used across multiple dashboards.
 * Implement parameterized dashboards and reusable visual components.

Quick Suite Development & AI-Powered Reporting

 * Design, develop, and maintain interactive dashboards, datasets, and visualizations in Amazon QuickSight (now part of Quick Suite).
 * Build and configure Quick Chat agents to enable natural language querying across business data sources.
 * Design Quick Spaces that group data, applications, and AI agents for specific business functions or teams.
 * Build high-performance dashboards optimized for large datasets and fast refresh times.
 * Implement row-level security (RLS) and governance controls for business users and AI agents.
 * Create standardized QuickSight templates and dashboard frameworks that can be reused across teams.
 * Design and maintain semantic layers and curated datasets for reporting and AI consumption.


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