Ideal Candidate Profile
- 10+ years of experience in Business Intelligence, Analytics Engineering, or Data Visualization.
- Strong hands-on experience with Amazon Quick Suite (including QuickSight).
- Experience designing AI-powered workflows, automation, or conversational AI agents (preferred but not required if willing to learn).
- Experience designing interactive dashboards and enterprise reporting solutions.
- Solid knowledge of SQL and data modeling.
- Experience working with AWS analytics services such as:
- Experience enabling self-service analytics for business users.
- Strong understanding of data visualization best practices.
- Experience optimizing reporting performance and cost efficiency.
- Ability to learn and adopt new AI-powered tools and platforms rapidly.
Role Overview
We are seeking a skilled Business Intelligence (BI) and AI Automation Engineer with strong expertise in Amazon Quick Suite (including QuickSight) and AWS analytics services to support business teams in building scalable, self-service reporting solutions and intelligent workflow automation.
The ideal candidate will partner closely with business stakeholders to understand reporting and automation needs, enable self-service analytics, design reusable reporting frameworks, build AI-powered workflows, and implement agentic AI solutions that reduce manual effort, compute costs, and improve operational efficiency across the organization.
This role requires a mix of technical BI development, data modeling, AI agent design, workflow automation, stakeholder collaboration, and cost optimization within AWS.
Key Responsibilities
1. 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.
- Optimize dashboards to reduce SPICE usage, refresh costs, and compute consumption.
2. AI Workflow Automation & Agent Development
- Design and implement Quick Flows to automate repetitive business tasks (weekly reports, data summaries, alert notifications).
- Build Quick Automate workflows for complex, multi-step enterprise processes (onboarding, approvals, data reconciliation).
- Create custom Chat Agents with tailored system prompts for specific business roles (sales, operations, finance, analytics).
- Configure Quick Research capabilities to enable deep business intelligence research with cited sources.
- Integrate Quick Suite with enterprise applications (Salesforce, ServiceNow, Slack, Jira, SharePoint) via 50+ built-in connectors and MCP protocols.
- Build reusable automation templates and workflow patterns that can be deployed across multiple business units.
3. Self-Service BI & AI Enablement
- Work directly with business users and analysts to enable self-service reporting and AI automation capabilities.
- Provide guidance on best practices for dashboard design, data consumption, and AI agent usage.
- Create reusable data models, templates, reporting accelerators, and workflow automation patterns.
- Develop training materials and documentation for business teams to build their own dashboards and configure AI agents.
- Conduct workshops and enablement sessions for business users on Quick Suite usage (QuickSight, Quick Chat, Quick Flows, Quick Automate).
4. Data Modeling & Dataset Engineering
- Design and maintain optimized datasets and data models for BI reporting and AI agent consumption.
- Build reusable data flows and data pipelines that serve multiple reporting and automation use cases.
- Work with engineering teams to integrate data from sources such as:
- AWS Redshift
- S3
- Athena
- RDS
- Glue Data Catalog
- Enterprise apps via Quick Suite connectors (Salesforce, Snowflake, SharePoint, Google Drive)
- Ensure datasets follow governance, quality, and consistency standards.
5. 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.
6. Performance Optimization & Cost Management
- Identify opportunities to reduce compute costs associated with reporting and AI automation workloads.
- Optimize use of SPICE vs direct query based on performance and cost requirements.
- Monitor and tune query performance across Athena, Redshift, and other data sources.
- Optimize Quick Suite automation workflows to minimize API calls, data transfers, and compute usage.
7. Collaboration with Business & Data Teams
- Partner with product owners, business analysts, and leadership teams to understand reporting and automation requirements.
- Translate business needs into scalable BI solutions and intelligent automation workflows.
- Work with data engineering teams to ensure required datasets are available and optimized.
- Participate in requirements gathering and reporting and automation roadmap planning.
8. Governance, Documentation & Standards
- Establish BI standards and governance for QuickSight dashboards and Quick Suite AI agents.
- Maintain documentation for:
- Datasets
- Metrics
- Dashboard logic
- Reporting frameworks
- AI agent configurations
- Workflow automation patterns
- Quick Suite integrations
- Ensure adherence to data security and access policies for both human users and AI agents.
- Implement naming conventions and versioning for dashboards, datasets, AI agents, and workflows.
Key Skills
- 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
- BI Architecture
- Self-Service Analytics Enablement
- Performance Optimization
- Cost Optimization
- API Integration & Enterprise Connectors
- Stakeholder Communication
Preferred Qualifications
- Hands-on experience with Amazon Quick Suite (Quick Chat, Quick Flows, Quick Automate, Quick Research, Quick Spaces).
- Experience building conversational AI agents, chatbots, or automation workflows.
- Experience with API integrations, webhooks, and enterprise application connectors (Salesforce, ServiceNow, Slack, Jira).
- Experience building QuickSight templates, themes, and reusable datasets.
- Experience with BI governance and semantic layer design.
- Familiarity with data pipelines and ETL frameworks.
- Knowledge of AWS cost optimization strategies.
- Experience supporting large-scale enterprise reporting environments.
- Experience with other BI tools such as Tableau, Power BI, or Looker.
- Experience with low-code/no-code automation platforms (Zapier, Power Automate, Workato).
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