Job description

Monetization Technology teams are building the next-generation monetization platforms to help millions of customers grow their businesses, utilizing our products like TikTok. Our team develops a wide variety of advertisements for numerous uses including feeds, live streaming, branding, measurement, targeting, search, vertical solutions, creative solutions, and business integrity. What You'll Do: - Own foundational targeting data and platform capabilities with high availability, accuracy, freshness, and scalability: - Base targeting dimensions: gender, age, geo, device, language, network, etc. - Audience & tagging system: definitions, hierarchy, refresh strategy, backfills, cross-device unification - Design and implement large-scale batch/stream pipelines: ingestion, ETL, aggregation, profile generation, tag updates, external serving - Build a reliable data quality framework: validation, lineage, monitoring/alerting, SLAs, automated backfill and repair - Provide standardized capabilities for ads delivery/strategy systems: - Audience package generation/management, tag query services, foundational targeting rule engine, access control & auditing - Collaborate with ML/product/compliance to ensure stable production rollout and iterative improvements (performance/reach/cost/UX) Minimum Qualifications: - BS+ in CS/SE/Data Engineering or related fields - 3+ years (adjustable) in data engineering/platform roles; able to own critical pipelines end-to-end - Strong SQL and data modeling; hands-on with big data stack (Spark/Hive/Kafka/Flink/Airflow, etc.) - Proficient in Java/Scala/Python; solid engineering and performance tuning skills - Strong ownership of data governance, definitions, quality and stability - Effective cross-functional communication and execution Preferred Qualifications: - Experience in ads/recommender data platforms: user profiles, tagging, audience segmentation, DMP/CDP - Real-time profile or low-latency serving at scale (high QPS, caching/consistency) - Privacy/compliance implementation experience (minimization, anonymization, access control, auditing)

Requirements

Must-haves

Own foundational targeting data and platform capabilities with high availability, accuracy, freshness, and scalability:
Base targeting dimensions: gender, age, geo, device, language, network, etc.
Audience & tagging system: definitions, hierarchy, refresh strategy, backfills, cross-device unification
Design and implement large-scale batch/stream pipelines: ingestion, ETL, aggregation, profile generation, tag updates, external serving
Build a reliable data quality framework: validation, lineage, monitoring/alerting, SLAs, automated backfill and repair
Provide standardized capabilities for ads delivery/strategy systems:
Audience package generation/management, tag query services, foundational targeting rule engine, access control & auditing
Collaborate with ML/product/compliance to ensure stable production rollout and iterative improvements (performance/reach/cost/UX)
BS+ in CS/SE/Data Engineering or related fields
3+ years (adjustable) in data engineering/platform roles; able to own critical pipelines end-to-end
Strong SQL and data modeling; hands-on with big data stack (Spark/Hive/Kafka/Flink/Airflow, etc.)
Proficient in Java/Scala/Python; solid engineering and performance tuning skills
Strong ownership of data governance, definitions, quality and stability
Effective cross-functional communication and execution

Nice-to-haves

Experience in ads/recommender data platforms: user profiles, tagging, audience segmentation, DMP/CDP
Real-time profile or low-latency serving at scale (high QPS, caching/consistency)
Privacy/compliance implementation experience (minimization, anonymization, access control, auditing)

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

mlsqljavapython

Platform: tiktok
Effort: medium