We can only accept candidates based in Ontario or Quebec. The Audio Market Opportunity Modern advertising marketers allocate spending through automated systems that interpret signals. For a channel to capture its fair share of budget, its inventory must be legible to those systems — standardized signals, structured metadata, and machine-readable supply pathways. For the next evolution of media buying, Audience Signal is even more consequential. Agentic buying — autonomous systems that independently interpret objectives, evaluate options, negotiate terms, and execute campaigns — is moving from concept to production. These systems don’t browse inventory the way a human planner does. They query structured environments, evaluate supply through machine-readable signals, and pass over inventory they cannot read. Our Mission Triton Digital builds the infrastructure layer that makes audio inventory legible to modern — and next-generation — advertising markets. Our platform enables broadcasters, independent podcasters, and streaming music services to participate in automated buying on equal terms with the major platforms, aggregating over 100 billion audio impressions per month across podcast, streaming, and broadcast radio inventory. The listener data team is at the heart of that mission. We enrich the listener profile to enable better advertising targeting through services including integration with Data Management Platforms (DMPs), the Profiler, the GeoIP service, and any other systems that serve the goal of making listener audiences continuously discoverable and actionable for buyers. The Role As our MLOps Data Engineer, you’ll be the bridge between data science and production systems — ensuring that models don’t just work in notebooks but thrive in real-world environments. You’ll design and automate CI/CD pipelines, optimize large-scale data processing with Apache Spark, and leverage Databricks to deliver machine learning solutions that are reliable, scalable, and fast. Your work will directly determine how quickly we can turn listener intelligence into structured, queryable signals that advertising systems — today’s DSPs and tomorrow’s agentic buyers — can act on. What You’ll Do - Design, implement, and maintain CI/CD pipelines for machine learning workflows using tools like GitHub Actions, Azure DevOps, or Jenkins. - Build and optimize data processing pipelines in Apache Spark (PySpark and Scala) for large-scale, distributed listener datasets. - Deploy and manage Databricks environments, ensuring efficient cluster usage, job scheduling, and cost optimization. - Collaborate with data scientists to productionize ML models, integrating them into scalable APIs or batch processing systems that feed real-time, machine-readable audience signals. - Implement automated testing, monitoring, and alerting for ML pipelines to ensure the reliability and reproducibility that certified buyers require. - Champion best practices in version control, model registry management, and environment reproducibility. - Help evolve our listener data infrastructure toward agent-compatible supply — live, structured, queryable data feeds that autonomous buying systems can discover and act on without human mediation. What You’ll Bring - Proven experience in Data Engineering, MLOps, and DevOps roles with a focus on automation and scalability. - Strong programming skills in Python, with hands-on experience in Apache Spark. Scala is a huge plus. - Advanced expertise in Databricks, including Delta Lake, structured streaming, feature engineering - Solid understanding of CI/CD principles and tools (e.g., GitHub Actions, Jenkins, Azure DevOps, GitLab CI, ArgoCD). - Familiarity with cloud platforms (AWS, Azure, or GCP) for data and ML workloads. - A problem-solving mindset and the ability to work closely with cross-functional teams. - Strong architectural mindset, capable of evaluating trade-offs across cost, performance, scalability, and maintainability when selecting tools and designing systems. - Experience working with containerized and orchestrated environments (Kubernetes / OpenShift), including deployment, scaling, and fault tolerance of data and ML workloads. - Advanced English required. French is an asset. - Familiarity with IAB data standards, programmatic advertising infrastructure, or AdTech data pipelines is a strong asset. Our benefits package includes - Fully remote position (must be based in ONTARIO or QUEBEC) - 4 weeks of vacation + 5 paid personal days annually - Group insurance programs as of your first day, including access to telemedicine and an EAP - Collective RRSP with matching contribution - Internet reimbursement and more *L’utilisation de l’anglais est nécessaire pour collaborer avec des équipes internes et internationaux, et pour accéder à des informations et des ressources. Triton Digital is an equal opportunity employer committed to fostering a diverse, equitable, and inclusive workplace where all employees are respected, supported, and enabled to perform at their highest potential.

• Design, implement, and maintain CI/CD pipelines for machine learning workflows using tools like GitHub Actions, Azure DevOps, or Jenkins. • Build and optimize data processing pipelines in Apache Spark (PySpark and Scala) for large-scale, distributed listener datasets. • Deploy and manage Databricks environments, ensuring efficient cluster usage, job scheduling, and cost optimization. • Collaborate with data scientists to productionize ML models, integrating them into scalable APIs or batch processing systems that feed real-time, machine-readable audience signals. • Implement automated testing, monitoring, and alerting for ML pipelines to ensure the reliability and reproducibility that certified buyers require. • Champion best practices in version control, model registry management, and environment reproducibility. • Help evolve our listener data infrastructure toward agent-compatible supply — live, structured, queryable data feeds that autonomous buying systems can discover and act on without human mediation.

• Design and implement Governance as Code frameworks for data management and control. • Define and operationalize Data Contracts using Open Data Contract standards. • Develop and manage semantic layers to ensure consistent data interpretation across systems. • Implement and maintain CI/CD pipelines for data governance using GitHub automations. • Collaborate with data engineers, analysts, and business stakeholders to align governance practices with business needs. • Ensure scalability, traceability, and compliance of data governance processes. • Contribute to the definition of best practices and standards for modern data architecture. • Support integration of governance frameworks within existing data platforms.

• Design, build, and maintain a scalable data platform on Databricks and AWS, following Medallion (Bronze/Silver/Gold) architecture principles. • Develop and manage data ingestion and ETL/ELT pipelines from core business systems, ensuring data quality, reliability, and performance. • Implement data quality monitoring (freshness, completeness, validity, anomaly detection) with automated alerting. • Establish and maintain Golden Record identity resolution and reverse sync across source systems. • Govern metadata, lineage, and permissions using Databricks Unity Catalog and platform standards. • Collaborate with cross-functional teams to deliver analytics- and AI-ready datasets that support business objectives. • Apply best practices for infrastructure as code, CI/CD, and version control for data pipelines. • Communicate effectively with stakeholders, manage ambiguity, and drive results in a fast-paced environment.