AWS Data Architect

Role: AWS Data Architect

Location: [Location(s) / Remote] – Hybrid – Clinton, NJ – 3 days in a week from office

Employment Type: [Full-time /Contract] – Contract

Duration: 6 months

Company Overview

Trianz is an applied AI solutions company that accelerates customer business transformation through AI powered “Transformation Services as a Software Model”. With 25+ years of transforming enterprises, we’ve evolved to a product-led, platform-driven organization serving global enterprises across Financial Services, Insurance, Healthcare, Hi-Tech, Manufacturing, and other industries.

With global presence across 4 continents, our platform portfolio under the unified Concierto brand delivers end-to-end transformations including solutions for Migrate, Manage, Maximize, Modernize, Insights & Agentic AI, and SecOps - delivered through strategic partnerships with leading hyperscalers.

We’re building the premier innovation-led organization in the digital transformation space through AI-first methodologies and data-driven excellence - RevolutionAIzingTransformations.

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….