Engineering Manager, Data in Canada Creek, Nova Scotia at Jobgether

This position is posted by Jobgether on behalf of a partner company. We are currently looking for a Engineering Manager, Data in Canada.

This role is a high-impact leadership opportunity at the intersection of data science, machine learning, and ad tech innovation. You will lead a globally distributed team responsible for building and scaling advanced data platforms that power attribution, experimentation, segmentation, and personalization systems. The role is central to shaping how data is used to optimize marketing performance, user growth, and product decision-making. You will work closely with cross-functional partners in product, growth, and marketing to translate complex data insights into actionable business strategies. This is a hands-on leadership position where technical depth meets strategic influence. You will play a key role in evolving ML systems, real-time data pipelines, and privacy-compliant analytics infrastructure. The environment is fast-moving, collaborative, and focused on building scalable, data-driven solutions with measurable impact.

In this role, you will lead and grow a distributed team of data scientists, ML engineers, and data platform engineers while setting a strong technical and strategic direction for data initiatives. You will define and drive the roadmap for machine learning systems and ad tech capabilities, ensuring continuous improvement in targeting, attribution, experimentation, and personalization.

• Lead and mentor a globally distributed engineering and data science team, supporting technical growth and career development.
• Design and execute the data science and ad tech strategy, including modeling, experimentation, and optimization initiatives.
• Architect and oversee ML pipelines, feature engineering systems, model training/serving infrastructure, and A/B testing frameworks.
• Manage real-time and batch data pipelines supporting ad events, attribution, and campaign performance analytics.
• Collaborate with cross-functional stakeholders to build audience segmentation, LTV/churn models, and incrementality measurement systems.
• Ensure robust, scalable, and privacy-compliant data systems aligned with global regulations (e.g., GDPR, CCPA, ATT).
• Promote engineering best practices in reproducibility, observability, model evaluation, and lifecycle management.
• Drive alignment between experimentation outputs and business outcomes to support growth and monetization strategies.

This role requires a strong blend of technical expertise in machine learning and data platforms, along with proven leadership experience managing high-performing teams in complex, data-driven environments.

• 5+ years of experience in software engineering, data engineering, or applied data science, including 3+ years in technical leadership roles.
• Strong background in machine learning, statistical modeling, causal inference, forecasting, and optimization techniques.
• Hands-on experience with modern data and ML tools such as Snowflake, BigQuery, Spark, Airflow, dbt, MLflow, and feature stores.
• Proven track record of building and deploying end-to-end ML systems in production environments (batch and real-time).
• Solid understanding of ad tech ecosystems, including attribution models, campaign structures, and performance measurement.
• Experience with audience segmentation, personalization systems, and data-driven marketing or CRM frameworks.
• Knowledge of privacy-preserving measurement techniques, including identity limitations and consent-based data systems.
• Strong communication, leadership, and stakeholder management skills across technical and non-technical audiences.
• Bachelor’s or Master’s degree in Computer Science, Engineering, Statistics, or related field (PhD is a plus).

• Competitive salary range: $120,000 – $160,000 (Canada-based, depending on experience and location).
• Comprehensive health coverage including medical, dental, and vision plans.
• Paid time off and supportive leave policies.
• Personalized career development roadmap with ongoing training and learning opportunities.
• Strong focus on well-being (physical, mental, and emotional support programs).
• Inclusive and collaborative culture emphasizing creativity, innovation, and teamwork.
• Opportunity to work on high-impact data systems shaping growth and monetization strategies.