AWS Data Architect

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 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