Senior Data Engineer, Ads Data (AI Native)

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Data Engineer, Ads Data (AI Native) based in Canada.

Join a remote-first technology organization building innovative products that help families stay connected and protected. As a Senior Data Engineer, you will play a key role in developing and scaling the data infrastructure that powers advertising analytics, campaign performance, and business intelligence. You will take ownership of critical data pipelines and platforms, improving their reliability, scalability, performance, and cost efficiency. Working closely with analysts, advertising strategists, and commercial teams, you will transform complex datasets into trusted, actionable insights. You will also embrace AI-native engineering practices, leveraging advanced AI tools to accelerate development, improve productivity, and strengthen technical capabilities. This is an opportunity to combine hands-on engineering, technical leadership, and business impact within a highly collaborative international environment.

Accountabilities

• Develop and optimize data infrastructure: Enhance, maintain, and scale existing batch and streaming pipelines, Delta Lake tables, and data workflows using Databricks.

• Build business-focused data solutions: Collaborate with analysts, advertising strategists, and sales teams to transform business requirements into reliable datasets, reporting solutions, and actionable insights.

• Manage data models and metrics: Develop and maintain dbt models, automated tests, semantic layers, and standardized business metrics to ensure consistency and accuracy across teams.

• Optimize performance and costs: Improve Spark and Databricks workloads through query optimization, partitioning, cluster configuration, and efficient resource allocation.

• Manage cloud infrastructure: Maintain data platform infrastructure using Terraform and AWS, ensuring scalable, secure, and reliable operations.

• Strengthen data quality and governance: Implement monitoring, data lineage, SLAs, alerting, access controls, and data contracts while ensuring compliance with privacy and regulatory requirements.

• Lead engineering excellence: Establish best practices for version control, CI/CD, testing, documentation, and code reviews while mentoring colleagues and contributing to technical roadmap decisions.

• Integrate diverse data sources: Consolidate and reconcile information from advertising platforms, CRM systems, external partners, and internal applications, including migrations from legacy reporting tools.

• Advance AI-native engineering: Develop tools, workflows, and AI agents that provide LLMs with relevant context and accelerate development, debugging, and system maintenance.

• Drive continuous improvement: Take ownership of foundational data pipelines and models, reduce processing times and infrastructure spending, and increase self-service access to reliable business information.

Requirements

• At least 5 years of professional data engineering experience, building and maintaining production-grade data platforms at enterprise scale.

• Advanced hands-on expertise in Apache Spark, including PySpark or Scala, with strong knowledge of partitioning, joins, shuffle behavior, caching, skew management, and performance optimization.

• Deep knowledge of Databricks, including Delta Lake, Unity Catalog, Jobs and Workflows, Databricks Asset Bundles, Structured Streaming, and SQL Warehouses.

• Strong proficiency in SQL and Python or Scala for data pipeline development, automation, and query optimization.

• Practical experience with dbt, including data modeling, incremental strategies, testing, documentation, and CI/CD integration.

• Hands-on experience