Compare your current skills to what this opportunity needs—we'll show you what you already have and what could strengthen your application.
Job Description
Job Title:
AWS Data Architect Location:
Clinton, NJ - 3
Days Onsite Duration:
3+
Months Contract Job Description:
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 - RevolutionAIzing Transformations. 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
