Data Engineer
Job Description
<p style="text-align: justify;">Pour la version française de cette description de poste, veuillez consulter le lien suivant / For the French version of this job description, please refer to the following link:</p> <ul style="text-align: justify;"> <li><a href="https://job-boards.greenhouse.io/appdirect/jobs/8653625002">Ingénieur(e) de données sénior</a></li> </ul> <p style="text-align: justify;"><strong>About AppDirect</strong></p> <p style="text-align: justify;">Become a digital, global citizen and enable the new generation of digital entrepreneurs around the world. AppDirect offers a subscription commerce platform to sell any product, through any channel, on any device - as a service. We power millions of subscriptions worldwide for organizations. We do this by our values-driven culture—one that enables you to Be Seen, Be Yourself, and Do Your Best Work.</p> <p style="text-align: justify;"><strong>About the Data Insights Team</strong></p> <p style="text-align: justify;">Our mission is to unify data from every business unit into a governed lakehouse and semantic layer, powering analytics, AI, reports, data sharing, and both internal and customer-facing dashboards.</p> <p style="text-align: justify;"><strong>About You</strong></p> <p style="text-align: justify;">We’re hiring a Senior Data Engineer for Data Insights in Montreal—someone with a Data as a Product mindset who builds production pipelines and models others can trust and reuse.</p> <p style="text-align: justify;">You’ll build production data products and lakehouse pipelines that power analytics and both internal and customer-facing dashboards—partnering across engineering and product, establishing clear data contracts, and leaving patterns others can reuse.</p> <p style="text-align: justify;"><strong>What You’ll Do and How You’ll Make an Impact</strong></p> <ul style="text-align: justify;"> <li><strong>Platform Architecture & Modeling:</strong> Design, build, and evolve the lakehouse data platform—reusable models and pipelines on Snowflake + dbt, with Databricks workloads where they fit—so analytics and product teams get reliable, governed data products.</li> <li><strong>Requirements & Stakeholder Partnership:</strong> Translate product and business requirements into data models and pipelines—working with PMs, BUs, and engineers so domain logic lands correctly in production.</li> <li><strong>Pipeline Modernization:</strong> Migrate legacy ETL processes to modern, efficient streaming and incremental pipelines, choosing Snowflake or Databricks based on fit.</li> <li><strong>Snowflake Performance & Cost:</strong> Operate and tune Snowflake for reliability and efficiency—warehouse sizing and utilization, clustering/partitioning where it pays off, and visibility into credit spend so scale doesn’t mean runaway cost.</li> <li><strong>AI-Assisted Operations:</strong> Apply AI-assisted development tools and spec-driven workflows to design, automate, and ship data pipelines and platform.</li> <li><strong>Self-Service Enablement:</strong> Facilitate scoped data onboarding and empower business unit engineers to build their own data products on top of our platform.</li> <li><strong>Customer-Facing Data Products:</strong> Build and evolve data behind customer-facing products—including the reporting service and App Insights—so pipelines and models deliver trustworthy product experiences.</li> <li><strong>Data Quality & Trust:</strong> Ensure data quality by driving and implementing robust data governance, automated testing, validation techniques, and lineage.</li> &l