Ingénieur(e) de données - Data Engineer

Foodtastic•Montreal, QC

About The Position

The primary responsibility is to assist with the integration and management of data from various systems including Point-of-Sale (POS) systems, social media platforms, and various other systems as required. By ensuring accurate and reliable data integration, this role enables data-driven decision-making, enhances operational efficiency, and fosters a comprehensive understanding of the business's performance across all brands. DATABRICKS PLATFORM OWNERSHIP Own and administer the Databricks workspace — clusters, jobs, notebooks, Delta architecture, and workspace configuration. Engineer reliable, high-performance ETL/ELT pipelines using PySpark and SQL to process POS, transactional, and operational data. Apply Delta Lake and Unity Catalog best practices: schema evolution, partitioning, ACID transactions, and version control. Continuously monitor and optimize cluster performance, job runtimes, and infrastructure cost across brands and environments. DATA INGESTION & PROCESSING Automate ingestion of daily sales, transactions, inventory, and operational data from POS systems, suppliers, and external feeds. Guarantee data completeness and integrity through validation, audit logging, and anomaly detection (e.g., missed stores, duplicate days). Standardize ingestion across structured and semi-structured formats (CSV, JSON, Parquet). SECURITY, GOVERANCE & COMPLIANCE Enforce enterprise-grade data security, encryption, and access control across Azure and Databricks. Implement RBAC, Unity Catalog governance, and workspace permissions aligned with IT and compliance standards. Manage credentials and secrets securely via Azure Key Vault and managed identities. Maintain lineage, metadata documentation, and version-controlled pipelines for all production systems. Apply infrastructure-as-code (Terraform/Bicep) and CI/CD for consistent and auditable deployments. DATA QUALITY, OBSERVABILITY & RELIABILITY Implement automated data validation frameworks and proactive alerting to catch anomalies (e.g., missing files, negative sales, outliers). Define and monitor SLAs/SLOs for data delivery and pipeline uptime. Partner with DevOps to enhance logging, monitoring, and backup strategies across environments. Drive a reliability-first culture ensuring every dataset powering reporting or decision-making is trusted and traceable. BI INTEGRATION & ENABLEMENT Deliver curated, analytics-ready data sets optimized for Power BI and enterprise reporting. Support BI teams with incremental refresh logic, optimized DAX/SQL performance, and metadata consistency. Validate upstream changes to prevent downstream reporting disruptions. Document transformations, schemas, and dependencies to maintain transparency and continuity. Enable brand-level and corporate-level visibility by structuring data for flexible aggregation and comparison.

Requirements

5+ years in data engineering or cloud data infrastructure, preferably within retail, restaurant, or multi-location operations.
Advanced proficiency in Databricks, Python, SQL, DevOps, Power BI.
Proven ability to build and maintain data pipelines powering operational dashboards and financial reporting.
Experience implementing data quality and observability frameworks.
Proficiency in both French and English due to the national scope of the work and the diverse stakeholders involved.
Strong written and verbal communication skills in both languages are essential to ensure clarity, consistency, and alignment in day-to-day interactions, project execution, and deliverables.

Responsibilities

Own and administer the Databricks workspace — clusters, jobs, notebooks, Delta architecture, and workspace configuration.
Engineer reliable, high-performance ETL/ELT pipelines using PySpark and SQL to process POS, transactional, and operational data.
Apply Delta Lake and Unity Catalog best practices: schema evolution, partitioning, ACID transactions, and version control.
Continuously monitor and optimize cluster performance, job runtimes, and infrastructure cost across brands and environments.
Automate ingestion of daily sales, transactions, inventory, and operational data from POS systems, suppliers, and external feeds.
Guarantee data completeness and integrity through validation, audit logging, and anomaly detection (e.g., missed stores, duplicate days).
Standardize ingestion across structured and semi-structured formats (CSV, JSON, Parquet).
Enforce enterprise-grade data security, encryption, and access control across Azure and Databricks.
Implement RBAC, Unity Catalog governance, and workspace permissions aligned with IT and compliance standards. Manage credentials and secrets securely via Azure Key Vault and managed identities.
Maintain lineage, metadata documentation, and version-controlled pipelines for all production systems.
Apply infrastructure-as-code (Terraform/Bicep) and CI/CD for consistent and auditable deployments.
Implement automated data validation frameworks and proactive alerting to catch anomalies (e.g., missing files, negative sales, outliers).
Define and monitor SLAs/SLOs for data delivery and pipeline uptime.
Partner with DevOps to enhance logging, monitoring, and backup strategies across environments.
Drive a reliability-first culture ensuring every dataset powering reporting or decision-making is trusted and traceable.
Deliver curated, analytics-ready data sets optimized for Power BI and enterprise reporting.
Support BI teams with incremental refresh logic, optimized DAX/SQL performance, and metadata consistency.
Validate upstream changes to prevent downstream reporting disruptions.
Document transformations, schemas, and dependencies to maintain transparency and continuity.
Enable brand-level and corporate-level visibility by structuring data for flexible aggregation and comparison.