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Apptoza Inc.

Principal Consultant – AWS Data Platforms & Agentic AI

Toronto, ON, Canada

Contract

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Job details

Job type: Contract
Apply by: Dec 3, 2026
Experience: 7+ years of strong AWS architecture and engineering experience.
Posting language: English
Posted: Nov 3, 2026

Original job description

Role - Principal Consultant – AWS Data Platforms & Agentic AI

Location - Toronto, ON

Principal Consultant – AWS Data Platforms & Agentic AI

About the Role

We are looking for a Principal Consultant – AWS Data Platforms & Agentic AI to provide technical leadership in architecting, building and scaling next-generation enterprise Data and Agentic AI platforms.

This is a senior, hands-on technology leadership role requiring a rare combination of AWS Data architecture, Agentic AI expertise, engineering depth and executive presence.

You should be equally comfortable presenting a technology vision to CTOs and Managing Directors, defining Reference Architectures/HLDs/LLDs, leading technical workshops, and working directly with engineers to solve complex design, implementation, and performance issues.

This is not a strategy-only architecture role. Strong hands-on engineering capability is essential.

What You'll Do

Lead architecture and engineering across Enterprise Data Platforms, Data Fabric, Data Products, Data Marketplace, BI and Agentic AI.

Define scalable Lakehouse and Medallion Architecture patterns for data ingestion, processing, storage, governance and consumption.

Architect Semantic Layers, Ontologies and Knowledge Graphs, including Amazon Neptune, to support analytics and Agentic AI.

Design secure Agentic AI solutions using Amazon Bedrock Agent Core, Runtime, Gateway, Identity and Model Context Protocol (MCP).

Establish patterns for Agent Identity, authentication, authorization, entitlements and governed agent-to-data access.

Build identity and entitlement architecture across IAM, IAM Identity Center, Cognito, Lake Formation, S3, Glue, Athena and Quick Sight.

Design fine-grained data security, including RLS/CLS, data governance, guardrails and access observability.

Industrialize Amazon QuickSight through dataset automation, SPICE optimization, security integration and platform governance.

Establish enterprise Agent telemetry, observability and evaluation across agents, models, MCP/tool calls and data access.

Define reusable Reference Architectures, HLDs, LLDs, engineering standards and implementation guardrails.

Lead architecture workshops, technical deep-dives and design reviews with engineering and senior leadership.

Hands-On Engineering: This role requires someone who can go beyond architecture and work under the hood.

You will:

Deep-dive into code, Spark jobs, data pipelines, SQL, APIs, IAM policies, infrastructure and platform configurations.

Diagnose complex design, integration, production and performance issues.

Optimize workloads across Spark/EMR, Athena, data pipelines and distributed services.

Build POCs and reference implementations.

Review solutions for scalability, security, resiliency and production readiness.

Act as a senior technical escalation point and mentor architects and engineers.

Technology Expertise

Strong architecture and hands-on expertise across several of the following:

AWS Data & Analytics

Amazon S3, AWS Lake Formation, Glue Data Catalog

Amazon EMR on EC2 / EMR Serverless

Spark / PySpark, Amazon Athena

Lakehouse & Medallion Architecture

Data Fabric, Data Products, Data Marketplace

Data Pipelines, Metadata, Lineage & Data Governance

Agentic AI & Knowledge Platforms

Amazon Bedrock / Agent Core

Agent Core Runtime, Gateway & Identity

Model Context Protocol (MCP)

Semantic Layer & Ontologies

Knowledge Graphs / Amazon Neptune

Agent Identity, Entitlements & AI Guardrails

Integration, Security & Platform Engineering

AWS IAM, IAM Identity Center, Cognito

Event Bridge, SNS, SQS, API Gateway, Lambda

Terraform, AWS Cloud Formation

Open Telemetry, Cloud Watch, Cloud Trail

QuickSight / SPICE / RLS / CLS

APIs, SQL, Angular and automation

What We're Looking For

12+ years across Data Platforms, Cloud Engineering, Platform Engineering or Architecture.

7+ years of strong AWS architecture and engineering experience.

Proven experience architecting and building enterprise-scale Data Platforms on AWS.

Deep understanding of Data Architecture, Medallion/Lakehouse, Data Products, Data Pipelines, Semantic Layers, metadata, governance and security.

Strong understanding of GenAI and Agentic AI architectures and their integration with enterprise data.

Experience taking solutions from architecture through implementation, production deployment and operationalization.

Ability to troubleshoot complex code, data, integration and performance issues.

Strong experience creating Reference Architectures, HLDs and LLDs.

Ability to lead executive conversations as well as hands-on technical workshops.

Financial Services or regulated-industry experience preferred.

Who Will Thrive in This Role

You are neither a strategy-only Enterprise Architect nor solely a delivery-focused Data Engineer.

You bring together:

Architecture Depth | Hands-on Engineering | Data & Agentic AI Vision | Executive Presence

You can articulate the future of Enterprise Data and Agentic AI to senior executives, translate that vision into detailed architecture, and then work alongside engineering teams to build, troubleshoot and optimize the solution.

You don't just define the architecture. You help build it and make it work in production.

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More about this job

Responsibilities

Lead the architecture and hands-on engineering of enterprise AWS data platforms and secure Agentic AI solutions, including lakehouse patterns, semantic layers, knowledge graphs, identity, governance, and observability. Define reusable architectures and engineering standards, troubleshoot and optimize production systems, and guide technical workshops, design reviews, and engineering teams.

Requirements

Requires 12+ years of experience in data platforms, cloud or platform engineering, or architecture, including 7+ years of AWS architecture and engineering experience. Candidates should have enterprise-scale AWS data platform and Agentic AI expertise, strong hands-on troubleshooting and implementation skills, and the ability to lead executive discussions and technical workshops; financial services or regulated-industry experience is preferred.

Skills

AWS Data Architecture
Enterprise Data Platforms
Lakehouse Architecture
Medallion Architecture
Agentic AI
Amazon Bedrock
Amazon Neptune
Data Governance
Data Security
AWS IAM
Apache Spark
PySpark
Amazon Athena
Amazon QuickSight
Terraform
Technical Leadership

Visa sponsorship

Not detected in the job text

Keywords

AWS
Amazon S3
AWS Lake Formation
AWS Glue Data Catalog
Amazon EMR
EMR Serverless
Apache Spark
PySpark
Amazon Athena
Amazon Bedrock
Bedrock Agent Core
Model Context Protocol
Amazon Neptune
Semantic Layers
Ontologies
Knowledge Graphs
Data Fabric
Data Products
Data Marketplace
Lakehouse Architecture
Medallion Architecture
Data Pipelines
Data Governance
AWS IAM
IAM Identity Center
Amazon Cognito
Amazon QuickSight
SPICE
Terraform
CloudFormation

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