Data Integration Engineer / Data Engineer

Company Description

Quinn AI is on a mission to transform strategic revenue planning, operational management, and sales execution with its 'Intelligent Revenue Operating System.' Leveraging advanced generative AI and machine learning models, Quinn AI delivers actionable insights, predictive analytics, and tailored recommendations for optimizing business outcomes. From annual sales planning to channel strategy and sales rep productivity, the company empowers organizations to achieve peak efficiency and performance.

Role Summary

Quinn AI is building the Agentic CRO — a digital revenue leader that helps B2B companies improve performance through data, automation, and AI. Our platform ingests, transforms, and enriches revenue data from dozens of source systems, turning it into AI-ready Intelligent Revenue Objects that power automated inspection, planning, and action.

We’re looking for a hands-on Data Integration Engineer in Vancouver, BC to own the data movement layer that makes this possible. You’ll build and maintain the integrations and pipelines that bring customer data into Quinn’s star schema, ensuring it meets the quality bar required for AI consumption. This is an execution-focused role within an existing technical environment — not platform architecture. Quinn is a seed-funded startup founded by two former Amazon executives with strong early traction and paying enterprise customers.

Key Responsibilities

Data Integration & Pipeline Development

• Design, build, and maintain integrations between customer source systems (CRM, ERP, analytics) and Quinn’s data platform
• Develop ETL/ELT pipelines for ingesting, staging, transforming, enriching, and validating revenue data across Quinn’s 5-step pipeline
• Build connectors to platforms such as HubSpot, Salesforce, NetSuite, and other B2B revenue systems
• Work with structured and semi-structured data across relational databases, flat files, APIs, and webhooks

Data Quality & Troubleshooting

• Troubleshoot data issues, integration failures, schema mismatches, and quality problems across customer deployments
• Profile datasets and improve data quality, consistency, and usability for downstream AI/ML consumers
• Ensure data conforms to Quinn’s star schema and meets certification standards for AI-ready KPIs

Collaboration & AI-Augmented Development

• Partner with engineering, product, data science, and customer success to translate requirements into working data solutions
• Support customer onboarding by resolving data integration issues alongside the CS team
• Use AI tools (Claude, Copilot, etc.) to accelerate development, debugging, and documentation
• Contribute to pipelines that feed Quinn’s ML models: forecasting, propensity scoring, segmentation, anomaly detection

Required Skills & Experience

• 3–7 years in data engineering, data integration, or ETL/ELT roles
• Strong SQL skills and deep comfort with relational databases (PostgreSQL preferred)
• Python experience for data processing, automation, and pipeline development
• Experience integrating with APIs (REST, webhooks), flat files (CSV, JSON), and external data sources
• Solid understanding of data transformation, validation, schema design, and troubleshooting
• Able to investigate issues independently across unfamiliar source systems
• Familiarity with Git and standard development workflows
• Comfortable using AI tools to improve speed and quality of work
• Strong communication skills and a bias for action

Nice to Have

• Star schema / dimensional modeling or data warehouse design
• Workflow orchestration tools (Airflow, Dagster, Prefect, or similar)
• AWS cloud data services (S3, Glue, Lambda, RDS, Redshift)
• Familiarity with B2B revenue data: CRM pipelines, bookings, ARR/MRR, sales hierarchies
• Data quality frameworks, metadata management, or dataset profiling tools
• NoSQL systems, event-driven architectures, or streaming data
• Exposure to LLMs, agent tooling, or Model Context Protocol (MCP)

What Success Looks Like

• Builds and maintains reliable customer integrations with minimal supervision — data flows cleanly from source to star schema
• Produces clean, validated, AI-ready datasets that downstream consumers (ML models, agents, dashboards) can trust
• Diagnoses and resolves data issues efficiently across multiple customer environments
• Uses AI as a practical accelerator without over-relying on it — understands the data deeply
• Improves team velocity through automation, documentation, and repeatable integration patterns

Why Join Quinn AI?

• Help build the data foundation for the world’s first Agentic CRO — redefining how B2B companies manage revenue performance
• Work directly with experienced founders and engineers who’ve scaled businesses to billions
• Solve real customer data problems with immediate impact and rapid iteration
• Join at the seed stage with meaningful equity and the opportunity to grow with the company