Senior Data Engineer

AppDirect•Montreal, QC

8d•$100,000 - $135,000•Hybrid

About The Position

AppDirect is seeking a Senior Data Engineer for their Data Insights team in Montreal. This role focuses on building production data products and lakehouse pipelines that power analytics and dashboards. The ideal candidate will have a "Data as a Product" mindset, building reusable and trustworthy data solutions. The Data Insights team's mission is to unify data from all business units into a governed lakehouse and semantic layer, supporting analytics, AI, reports, data sharing, and dashboards. This role involves designing and evolving the data platform, translating business requirements into data models, modernizing ETL processes, optimizing Snowflake performance and cost, applying AI-assisted development tools, enabling self-service data onboarding, building customer-facing data products, ensuring data quality and trust, managing metadata, and researching new technologies. The position also requires authoring and maintaining documentation.

Requirements

Strong understanding of AI-assisted development workflows, with proven hands-on experience using tools such as Cursor, Claude, OpenCode, GitHub Copilot, or ChatGPT.
Experience with spec-driven development: turning requirements into clear specs/plans and acceptance criteria, then implementing them.
2+ years building and operating production data pipelines and models on Snowflake using SQL, Python, and dbt.
2+ years of hands-on experience building modular, version-controlled, and tested data models using dbt (data build tool).
2+ years of experience with AWS cloud services.
Solid understanding of data quality, lineage, validation techniques, and data governance.
Ability to communicate complex technical concepts effectively to both technical and non-technical stakeholders, gather requirements, and work effectively in a distributed team.

Nice To Haves

Hands-on Databricks experience (workspaces, jobs/workflows, Spark SQL/PySpark, Delta Lake).
Exposure to Fivetran (or similar ELT connectors) for reliable source-to-warehouse ingestion, connector governance, and schema-evolution handling.
Exposure to Cube.dev (or a similar semantic/metrics layer) for governed, self-serve analytics and consistent metrics.
Strong preferred experience building and maintaining real-time data solutions using streaming platforms like Apache Kafka.

Responsibilities

Design, build, and evolve the lakehouse data platform, including reusable models and pipelines on Snowflake + dbt, with Databricks workloads where appropriate.
Translate product and business requirements into data models and pipelines, collaborating with product managers, business units, and engineers.
Migrate legacy ETL processes to modern, efficient streaming and incremental pipelines, selecting Snowflake or Databricks based on suitability.
Operate and tune Snowflake for reliability and efficiency, managing warehouse sizing, utilization, clustering/partitioning, and monitoring credit spend.
Apply AI-assisted development tools and spec-driven workflows to design, automate, and ship data pipelines and platform components.
Facilitate scoped data onboarding and empower business unit engineers to build their own data products on the platform.
Build and evolve data supporting customer-facing products, including the reporting service and App Insights.
Ensure data quality through robust data governance, automated testing, validation techniques, and lineage tracking.
Curate rich metadata in Unity Catalog and Snowflake to support downstream consumption, including AI agents and the semantic layer (Cube.dev).
Research solutions to complex problems and lead proof-of-concepts for emerging technologies.
Author and maintain high-quality documentation for knowledge sharing and AI-assisted workflows.