AWS Data Architect

Position: AWS Data Architect

Location: Hybrid – Clinton, NJ – 3 days in a week from office

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 12+ 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 Des…