Sr. Data Engineer

Key Responsibilities

• Customer Intelligence Platform & Data Product Ownership
• Serve as the technical lead and owner for Customer360, driving roadmap planning, architecture, delivery, support, and ongoing enhancements.
• Design and maintain customer data products that bring together identity, preferences, consent, transaction, engagement, and behavioral data.
• Collaborate with business and technology stakeholders to prioritize initiatives, define requirements, and deliver measurable business value.
• Advance our customer intelligence capabilities, including audience segmentation, personalization, activation, and AI-enabled insights.
• Enterprise Data Engineering & Architecture
• Design, build, test, and optimize scalable batch and near real-time data pipelines using Azure Databricks, Spark, PySpark, Python, SQL, Delta Lake, Azure Data Factory, and dbt.
• Build trusted, production-ready data products using modern lakehouse architecture and best practices.
• Develop reusable frameworks, components, and integration patterns that improve engineering efficiency and scalability.
• Create robust analytical and dimensional data models that support reporting, self-service analytics, machine learning, and AI use cases.
• Audience Segmentation & Activation
• Own and support data integrations between Databricks, Braze, and other customer engagement platforms.
• Design reliable and secure pipelines that enable audience activation across marketing, advertising, personalization, and media channels.
• Implement scalable integration patterns including API connectivity, incremental processing, schema evolution, monitoring, and data reconciliation.
• Partner with Marketing and Media teams to enable audience creation, campaign activation, measurement, and future Marketing360 capabilities.
• Reliability, Governance & Optimization
• Ensure the reliability and performance of customer data products through monitoring, alerting, data quality controls, incident management, and continuous improvement practices.
• Implement and support data governance standards using Unity Catalog, including access controls, lineage, auditing, and data ownership.
• Ensure customer data solutions meet privacy, security, consent, and regulatory requirements.
• Continuously optimize Databricks workloads and infrastructure to improve performance, scalability, and cost efficiency.
• Technical Leadership & Collaboration
• Lead architecture discussions, design reviews, code reviews, and technical problem-solving across the Data Engineering team.
• Mentor team members and champion engineering best practices across coding, testing, CI/CD, governance, and support.
• Support and provide coverage across Cineplex's broader data ecosystem, including Customer360, Media360, Operations360, and shared data platforms.
• Identify and proactively manage technical, operational, quality, security, and delivery risks

Requirements

• Degree or diploma in Computer Science, Software Engineering, Information Systems, Data Analytics, or a related field, or equivalent practical experience.
• Databricks Data Engineer and/or Microsoft Azure Data Engineer certification is considered an asset.
• 6+ years of experience in data engineering, data platform engineering, or data architecture, including ownership of production data solutions.
• 3+ years of hands-on experience delivering solutions on Azure Databricks or a similar Spark-based lakehouse platform.
• Strong expertise in SQL, Python, PySpark, Apache Spark, Delta Lake, data modelling, and modern ETL/ELT development practices.
• Hands-on experience with Azure Databricks, Azure Data Factory, dbt, Git, Azure DevOps, CI/CD pipelines, automated testing, and production deployments.
• Experience building scalable, governed data products and pipelines, with a strong understanding of data quality, lineage, security, performance optimization, and operational support.
• Experience designing and supporting secure integrations across APIs, SaaS platforms, databases, and cloud-based data ecosystems.
• Ability to translate complex business requirements into scalable, reliable, and business-ready data solutions.
• Experience with customer data platforms, Customer360 solutions, audience segmentation, personalization, marketing activation, Braze, or related customer intelligence technologies is considered an asset.
• Strong leadership, communication, stakeholder management, mentoring, and cross-functional collaboration skills.

Inclusion & Diversity

Accessibility

Use of AI