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:

• Ingénieur(e) de données sénior

About the Data Insights Team

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.

About You

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.

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.

What You'll Do and How You'll Make an Impact

• Platform Architecture & Modeling: 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.
• Requirements & Stakeholder Partnership: Translate product and business requirements into data models and pipelines-working with PMs, BUs, and engineers so domain logic lands correctly in production.
• Pipeline Modernization: Migrate legacy ETL processes to modern, efficient streaming and incremental pipelines, choosing Snowflake or Databricks based on fit.
• Snowflake Performance & Cost: 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.
• AI-Assisted Operations: Apply AI-assisted development tools and spec-driven workflows to design, automate, and ship data pipelines and platform.
• Self-Service Enablement: Facilitate scoped data onboarding and empower business unit engineers to build their own data products on top of our platform.
• Customer-Facing Data Products: Build and evolve data behind customer-facing products-including the reporting service and App Insights-so pipelines and models deliver trustworthy product experiences.
• Data Quality & Trust: Ensure data quality by driving and implementing robust data governance, automated testing, validation techniques, and lineage.
• Metadata Management: Curate rich metadata in Unity Catalog and Snowflake to power downstream consumption, including AI agents and our semantic layer (Cube.dev).
• Research & Innovation: Research solutions to complex problems and lead proof-of-concepts to evaluate emerging technologies.
• Documentation & Culture: Author and maintain high-quality documentation to support knowledge sharing and AI-assisted workflows.

What we're looking for

• AI-Assisted Development: Strong understanding of AI-assisted development workflows, with proven hands-on experience using tools such as Cursor, Claude, OpenCode, GitHub Copilot, or ChatGPT to improve efficiency, automation, and code quality.
• Spec-Driven Development: Experience with spec-driven development: turning requirements into clear specs/plans and acceptance criteria, then implementing them (including AI-agent-assisted workflows).
• Snowflake Expertise (core skillset): 2+ years building and operating production data pipelines and models on Snowflake using SQL, Python, and dbt-shipping reliable ELT/transformations, owning quality and performance in production.
• dbt Mastery: 2+ years of hands-on experience building modular, version-controlled, and tested data models using dbt (data build tool), treating transformation as software engineering (Git workflows, code review, automated tests).
• AWS: 2+ years of experience with AWS cloud services.
• Data Governance: A solid understanding of data quality, lineage, validation techniques, and data governance.
• Collaboration & Communication: Ability to communicate complex technical concepts effectively to both technical and non-technical stakeholders, gather requirements, and work effectively in a distributed team.

Preferred / Additional Strengths

• Databricks: Hands-on Databricks experience (workspaces, jobs/workflows, Spark SQL/PySpark, Delta Lake) to contribute to lakehouse work alongside Snowflake.
• Managed Ingestion (Fivetran): Exposure to Fivetran (or similar ELT connectors) for reliable source-to-warehouse ingestion, connector governance, and schema-evolution handling.
• Semantic Layer (Cube.dev): Exposure to Cube.dev (or a similar semantic/metrics layer) for governed, self-serve analytics and consistent metrics across products and consumers.
• Real-Time Streaming: Strong preferred experience building and maintaining real-time data solutions using streaming platforms like Apache Kafka.

#hybrid

The salary band listed below reflects the expected annual base salary or OTE (on-target earnings) for this role at AppDirect and may be subject to change.

Base salary or OTE is just one component of AppDirect's total compensation package. In addition to base pay, regular employees may be eligible for performance-based bonuses and a full range of benefits.

Canada Compensation Band

$100,000-$135,000 USD

L'échelle salariale indiquée ci-dessous correspond au salaire de base annuel prévu ou à la rémunération cible (OTE) pour ce poste chez AppDirect et est susceptible d'être modifiée.

Le salaire de base ou la rémunération cible (OTE) ne constitue qu'une composante du package de rémunération global proposé par AppDirect. Outre le salaire de base, les employés permanents peuvent prétendre à des primes liées à la performance et à une gamme complète d'avantages sociaux. Fourchette

Échelle salariale au Canada

$100,000-$135,000 USD