Director of Engineering, Data
New
X
XsollaAdTech
CanadaFull-TimeDirector
Salary120,000 - 160,000 USD per year
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Job Details
- Experience
- 5+ years of experience in software/data engineering or applied data science, with 3+ years managing technical teams
- Required Skills
- Apache AirflowMachine LearningMLFlowSnowflakeData engineeringSparkBigQuerydbt
Requirements
- 5+ years of experience in software/data engineering or applied data science.
- 3+ years managing technical teams in ML, analytics, or ad tech domains.
- Deep understanding of machine learning and statistical modeling, including regression, classification, causal inference, uplift modeling, and forecasting.
- Hands-on experience with ML/data platforms such as Snowflake, BigQuery, Spark, Airflow, dbt, MLFlow, and feature stores.
- Proven experience in architecting and deploying end-to-end ML systems into production (batch and real-time).
- Knowledge of ad tech ecosystems, including campaign hierarchies, attribution models, and creative performance tracking.
- Familiarity with audience management, segmentation, and personalization frameworks.
- Experience with privacy-preserving measurement, including SKAdNetwork, GAID/IDFA deprecation, and consent systems.
- Excellent leadership, communication, and stakeholder management skills.
- Bachelor’s or Master’s in Computer Science, Engineering, Statistics, or a related field; PhD is a plus.
Responsibilities
- Lead and grow a high-performing, distributed team of data scientists, ML engineers, and data platform engineers.
- Define and execute the data science and ad tech roadmap, advancing initiatives in user modeling, campaign optimization, targeting, and personalization.
- Architect and manage ML pipelines and experimentation frameworks, including feature engineering, training pipelines, model serving, A/B testing, and causal inference systems.
- Oversee real-time pipelines for ad events, enabling responsive attribution and performance optimization.
- Collaborate with Product, Growth, and Marketing to develop audience scoring, LTV/churn models, and incrementality testing.
- Ensure scalable, privacy-compliant data infrastructure aligned with GDPR, CCPA, and ATT.
- Foster engineering excellence with a focus on reproducibility, model evaluation, observability, and model lifecycle management.
- Drive a strong feedback loop between experimentation and business outcomes.
- Mentor engineers and scientists on career development, technical depth, and cross-functional leadership.
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