Engineering Manager, Data

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 (e.g., impressions, clicks, conversions), enabling responsive attribution and performance optimization.
• Collaborate with Product, Growth, and Marketing to develop audience scoring, LTV/churn models, and incrementality testing for media measurement and bidding efficiency.
• Ensure scalable, privacy-compliant data infrastructure aligned with GDPR, CCPA, and ATT, including support for SKAdNetwork, CMPs, and identity frameworks.
• 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, translating data science insights into product and go-to-market wins.
• Mentor engineers and scientists on career development, technical depth, and cross-functional leadership.

Qualifications & Skills:

• 5+ years of experience in software/data engineering or applied data science, with 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 (multi-touch, view-through), and creative performance tracking.
• Familiarity with audience management, segmentation, and personalization frameworks in programmatic or CRM marketing.
• Experience with privacy-preserving measurement, including support for SKAdNetwork, GAID/IDFA deprecation, and consent systems.
• Excellent leadership, communication, and stakeholder management skills across technical and non-technical audiences.
• Bachelor’s or Master’s in Computer Science, Engineering, Statistics, or a related field. PhD is a plus.