# Job Description: AWS Data Architect
Location: Clinton, New Jersey, USA Work Model: Hybrid, 3 days onsite per week Experience: 15+ years overall, 6+ years in AWS Data Architecture, and 9+ years in Data Engineering Employment Type: Full-time
## Role Overview
We are seeking a highly experienced and hands-on AWS Data Architect to lead the design, development, implementation, and governance of enterprise-scale data platforms on AWS.
The ideal candidate will possess deep expertise in cloud data architecture, data engineering, data processing, analytics, and data governance. This role requires a strong technical leader who can take ownership of end-to-end data architecture while actively contributing to implementation, optimizing data pipelines, and establishing best practices across engineering teams.
The candidate must be comfortable working with business stakeholders, engineering teams, and senior technology executives, including CTO-level stakeholders. Excellent communication, architectural decision-making, and technical leadership skills are essential.
## Mandatory Qualifications
- 15+ years of overall professional experience.
- Minimum 6 years of hands-on AWS Data Architecture experience.
- Minimum 9 years of Data Engineering experience.
- Strong hands-on expertise in AWS data services and cloud-based data platforms.
- Extensive experience designing and implementing scalable enterprise data architectures.
- Strong programming and data processing skills using Python, PySpark/Spark, and advanced SQL.
- Excellent communication skills, including experience interacting with CTO-level stakeholders.
- Proven ability to lead technical discussions, make architectural decisions, and mentor engineering teams.
- Willingness to work in a hybrid environment in Clinton, New Jersey, three days per week onsite.
## Key Responsibilities
### 1. Data Architecture and Solution Design
- Define and own end-to-end data architecture on AWS, covering data ingestion, storage, processing, transformation, and consumption.
- Design scalable, secure, highly available, and cost-effective data platforms.
- Develop modern data lake and lakehouse architectures to support enterprise analytics and reporting.
- Establish standards for data modeling, partitioning, metadata management, data retention, and lifecycle management.
- Design architectures supporting both batch and real-time data processing.
- Evaluate technology options and recommend suitable architectural solutions based on business and technical requirements.
- Lead architecture reviews and ensure adherence to enterprise architecture standards and best practices.
### 2. Hands-On Data Engineering
- Design, develop, implement, and optimize data pipelines using AWS Glue, Amazon EMR, AWS Lambda, and AWS Step Functions.
- Build and maintain ETL/ELT pipelines using Python, PySpark, Apache Spark, and SQL.
- Design and implement data storage and processing solutions using Amazon S3, Amazon Redshift, Amazon RDS, and Amazon DynamoDB.
- Develop reusable data transformation frameworks and standardized engineering components.
- Implement data ingestion processes from databases, APIs, applications, and other enterprise data sources.
- Troubleshoot data pipeline failures, data quality issues, and processing bottlenecks.
- Provide technical guidance to data engineers while contributing directly to implementation when required.
### 3. Data Governance, Security, and Compliance
- Establish data governance frameworks covering data ownership, metadata, cataloging, lineage, and data quality.
- Implement data access controls and security policies using AWS IAM and AWS Lake Formation.
- Work with AWS Glue Data Catalog to organize and manage enterprise datasets.
- Design row-level security, encryption strategies, and appropriate access controls for sensitive business data.
- Ensure data platforms follow organizational security standards and applicable regulatory requirements.
- Establish processes for monitoring data quality, maintaining data integrity, and ensuring secure data access.
### 4. Performance Optimization and Cost Management
- Optimize AWS data pipelines for processing performance, reliability, and cost efficiency.
- Improve query performance across Amazon Redshift and S3-based data architectures.
- Optimize data partitioning, storage formats, processing strategies, and resource utilization.
- Identify bottlenecks in data ingestion, transformation, and reporting workloads.
- Improve dashboard performance, dataset refresh times, and analytical query efficiency.
- Optimize SPICE datasets and related BI performance where Amazon QuickSight is used.
- Establish monitoring and optimization practices to improve the overall efficiency of data platforms.
### 5. Data Migration and Modernization
- Lead migration of legacy data platforms, on-premises databases, and existing analytics environments to AWS.
- Develop modernization strategies for legacy data warehouses, Hadoop ecosystems, and reporting platforms.
- Define migration approaches that minimize disruption while maintaining data quality and business continuity.
- Support large-scale business intelligence and reporting migrations, including migration to Amazon QuickSight.
- Evaluate cloud-native technologies and recommend improvements to existing data architecture.
- Work with cross-functional teams to plan, execute, validate, and optimize data migration initiatives.
### 6. Reporting Frameworks and Reusable Components
- Develop reusable reporting templates, dataset templates, dashboard frameworks, and QuickSight themes.
- Establish standardized KPIs, calculated fields, and business metric definitions.
- Design modular reporting components that can be reused across multiple dashboards and business functions.
- Implement parameterized dashboards and reusable visual components.
- Develop curated datasets and semantic layers to support consistent reporting and analytics.
- Design reusable AI agents and workflow templates for business process automation.
- Establish standards for dashboard development, data presentation, and reporting governance.
### 7. Amazon QuickSight, Quick Suite, and AI-Powered Reporting
- Design, develop, and maintain interactive dashboards, datasets, and visualizations using Amazon QuickSight, now part of Amazon Quick Suite.
- Build and configure Quick Chat agents to support natural-language querying across business data sources.
- Design Quick Spaces to organize data, applications, and AI agents for specific business functions.
- Build high-performance dashboards optimized for large datasets and fast refresh cycles.
- Implement row-level security and governance controls for business users and AI-powered reporting experiences.
- Create standardized QuickSight templates and reusable dashboard frameworks.
- Design and maintain semantic layers and curated datasets for reporting and AI consumption.
- Support modular AI-agent design, workflow automation, and reusable reporting solutions across enterprise functions.
### 8. Technical Leadership and Stakeholder Collaboration
- Act as the primary owner of data architecture decisions and technical solution design.
- Lead technical discussions, architecture presentations, and design reviews.
- Communicate complex technical concepts and architectural trade-offs clearly to business leaders and CTO-level stakeholders.
- Collaborate with business, analytics, cloud infrastructure, and engineering teams to translate business requirements into technical solutions.
- Mentor data engineers and establish coding, documentation, testing, and architectural best practices.
- Drive technical problem-solving, risk assessment, and continuous improvement initiatives.
- Provide architectural recommendations that balance scalability, security, maintainability, performance, and cost.
## Required Technical Skills
- AWS Services: Amazon S3, AWS Glue, Amazon Redshift, AWS Lambda, AWS Step Functions, Amazon EMR, Amazon RDS, Amazon DynamoDB.
- Data Processing: Python, PySpark, Apache Spark, advanced SQL, ETL/ELT pipeline development.
- Data Architecture: Data lakes, lakehouse architecture, data warehouses, dimensional modeling, normalized data modeling, metadata management, data cataloging, and lineage.
- Security and Governance: AWS IAM, AWS Lake Formation, encryption, row-level security, data access controls, and data quality.
- BI and Reporting: Amazon QuickSight, Quick Suite, dashboard design, semantic layers, curated datasets, and reporting optimization.
- Data Integration: API-based ingestion, batch processing, data transformation, workflow orchestration, and distributed data processing.
## Preferred Qualifications
- Experience with dbt, Apache Airflow, Snowflake, and modern data stack technologies.
- Experience with streaming technologies such as Amazon Kinesis or Apache Kafka.
- Familiarity with Tableau, Power BI, or other business intelligence platforms.
- Experience with microservices-based data flows and API integrations.
- AWS certifications such as AWS Certified Solutions Architect or relevant AWS data and analytics certifications.
- Experience with enterprise-scale data transformation, cloud modernization, and regulatory compliance.
- Exposure to AI-agent design, AI-powered reporting, and business workflow automation.
## Ideal Candidate Profile
The ideal candidate is a senior, hands-on AWS Data Architect who combines deep technical expertise with strong architectural ownership and leadership capabilities.
They should have a proven track record of designing and implementing enterprise data platforms, building scalable data pipelines, optimizing AWS analytics solutions, and establishing data governance standards. They must be equally comfortable contributing to technical implementation and leading high-level architecture discussions with senior executives.
Candidates must meet all three mandatory experience requirements: 15+ years of overall experience, at least 6 years of AWS Data Architecture experience, and at least 9 years of Data Engineering experience.
