Posted 11 months ago

Engineering Manager, Data

CanadaRemoteFull-time

AI Summary

Senior Engineering Manager leading data science and data engineering, shaping data platform and ad-tech capabilities; oversees a distributed team delivering data-powered attribution, experimentation, and personalization.

About this role

ABOUT YOU

We are looking for a Senior Engineering Manager to lead our Data Science & Data Engineering team. This role focuses on innovation within data infrastructure, statistical modeling, and advertising technology. You will manage a globally distributed team, scaling and building data-powered systems for attribution, experimentation, campaign analytics, user segmentation, and personalization. This is a unique opportunity to shape Xsolla’s data platform and ad tech capabilities, enabling smarter decision-making and more effective growth strategies for our publishing and monetization products.

ABOUT US

Xsolla is a global commerce company with robust tools and services to help developers solve the inherent challenges of the video game industry. From indie to AAA, companies partner with Xsolla to help them fund, distribute, market, and monetize their games. Grounded in the belief in the future of video games, Xsolla is resolute in the mission to bring opportunities together, and continually make new resources available to creators. Headquartered and incorporated in Los Angeles, California, Xsolla operates as the merchant of record and has helped over 1,500+ game developers to reach more players and grow their businesses around the world. With more paths to profits and ways to win, developers have all the things needed to enjoy the game.

For more information, visit xsolla.com.

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.

Skills

A/b TestingAirflowAudience SegmentationBigQueryCausal InferenceConsent SystemsDbtEnd-to-end ML SystemsFeature StoresGAID/IDFA DeprecationLTV/churn ModelingMLflowMulti-touch AttributionPersonalizationPrivacy-preserving MeasurementReal-time Data PipelinesSKAdNetworkSnowflakeSpark

Engineering Manager, Data

About this role

Responsibilities:

Qualifications & Skills:

Skills

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