Position: AWS Data Architect
Location: Clinton, NJ (Hybrid)
Type: Full-Time
Job Description
Recent update from the client:
Quick recap of requirements:
- 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
Role Overview:
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.
Key Responsibilities:
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.
Ideal Candidate Profile:
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
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