Job Title: Senior Data Engineer Location: Montreal, Canada (or Remote) Reports to: Head of Engineering

Company Overview FrontlineIQ is an AI-powered coaching and sales intelligence platform designed to help B2C sales teams improve performance through data-driven insights. We empower sales managers with real-time coaching tools, goal-setting automation, and actionable analytics to drive revenue and engagement. Our initial focus is on retail, but our vision extends far beyond, transforming frontline sales performance across multiple industries. With a growing customer base and strong support from UP.Labs Venture Studio, FrontlineIQ is scaling rapidly. We’re looking for experienced, action-oriented leaders who know how to execute and drive results to join our founding team and help shape the future of AI-driven sales performance.

Role Overview FrontlineIQ is seeking a Senior Data Engineer with a passion for building scalable data infrastructure and ML pipelines. You’ll be a key technical leader, driving data architecture decisions, collaborating across engineering and AI teams to deliver data-driven insights at scale.

What You’ll Own Technical Leadership & Architecture Drive data platform technical vision - Shape our Airflow DAGs, dbt transformations, and ML pipeline architecture Own data pipeline delivery - Design, implement, and maintain ETL/ELT pipelines and data warehouse integrations Continuous improvement - Identify opportunities for data quality, performance optimization, and infrastructure enhancement Modern data standards - Champion emerging best practices in data engineering, ML ops, and cloud architecture Team Leadership & Collaboration Cross-functional collaboration - Work closely with AI/ML teams, product, and backend engineers to deliver data-driven features Technical standards - Establish and maintain data quality standards, testing practices, and deployment workflows Knowledge sharing - Lead technical discussions, document data architecture decisions, and contribute to engineering culture

Technical Requirements Core Requirements 5+ years professional experience with data engineering Expert-level understanding of ETL/ELT pipelines, data modeling, and distributed systems Production experience with Apache Airflow for workflow orchestration Passion for data engineering technologies, methodologies, frameworks, and optimization AI-enhanced development - Proficient with AI coding to maximize productivity Preferred Experience LLM integrations - Experience with AI/LLM APIs and orchestration frameworks Apache Airflow - Building and maintaining complex DAGs for data orchestration dbt (Data Build Tool) - SQL transformations and data modeling in cloud data warehouses Cloud platforms - Cloud platforms (AWS, Fly.io), containerization with Docker Data warehouses - Cloud data warehouse platforms, data lake architectures OLTP databases - for data storage and real-time data ingestion Testing expertise - Unit testing, data quality testing, and pipeline monitoring Claude Code - prompt engineering, rules, mcp, subagents, models, etc are tools in your toolbelt

Leadership Qualities We Value Ownership Mindset Proactive problem-solving - Identify data quality issues and pipeline failures before they impact downstream systems Product thinking - Understand business metrics and analytics needs, not just technical requirements Quality advocacy - Champion data quality, testing standards, and pipeline reliability Continuous improvement - Stay current with data engineering trends and evaluate new technologies Communication & Collaboration Technical communication - Explain complex data architectures clearly to both technical and non-technical stakeholders Code review expertise - Provide constructive feedback on application code, schema, queries, and pipeline design Collaborative impact - Satisfaction in unblocking ML engineers and analysts with reliable data infrastructure Documentation - Communicate data schema changes, pipeline updates, and architectural decisions

What Makes You Stand Out Innovation & Passion Open source contributions - Active in the data engineering community with public repositories or contributions Thought leadership - Blog posts, conference talks, or technical writing about data engineering and ML ops Experimentation - Actively explores emerging data technologies and evaluates their potential impact Data-driven mindset - Strong understanding of business metrics and how data drives product decisions Startup Experience Fast-paced environment - Comfortable with rapid iteration and evolving data requirements Full-stack awareness - Understanding of application systems, APIs, and end-to-end data flow Growth mindset - Eager to learn new data technologies and adapt to evolving business intelligence needs

Why FrontlineIQ? At FrontlineIQ, we eliminated the typical risks of early-stage startups by launching the company out of UP.Labs Venture Studio, backed by world-class investors and a strong foundation of industry partnerships. This allows us to offer competitive salaries, meaningful equity, and comprehensive benefits while maintaining the agility and impact of a fast-scaling company.

As a team member, you’ll work alongside experienced operators who know how to execute, not just strategize. You’ll have the opportunity to shape an industry-defining platform, collaborate with top experts, and make a tangible impact on how B2C sales teams perform and grow—starting in retail and expanding far beyond. If you thrive in a high-performance environment where getting things done matters as much as vision, FrontlineIQ is the place to build something extraordinary.

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