Senior Data Engineer

• Write and defend diagnostic SQL queries against large-scale production datasets
• Build and maintain ingestion pipelines for bid, win, and impression logs into BigQuery
• Harmonize fields across independently designed datasets and maintain versioned field mappings
• Develop point-in-time-correct feature tables and aggregation pipelines
• Design and maintain conversion and labeling pipelines with delayed label handling
• Own the data serving write path, schema contracts, publishing flows, and freshness SLOs
• Build experimentation infrastructure including traffic splitting and reporting pipelines
• Perform large-scale historical backfills and safe reprocessing after mapping changes
• Implement data isolation and safe-aggregation controls for advertiser data protection
• Develop automated data quality validation frameworks
• Collaborate closely with Customer engineers and prepare operational documentation
• Contribute to architecture discussions and platform scalability improvements

• 5+ years of experience in Data Engineering
• At least 2 years of experience working with production ML or large-scale analytics pipelines
• Expert-level SQL skills including window functions and incremental processing patterns
• Strong Python skills for production-grade pipeline development
• Hands-on experience with Spark or PySpark
• Experience designing ETL / ELT pipelines with Airflow, Cloud Composer, Dagster, or similar tools
• Experience working with cloud data warehouses at scale, preferably BigQuery
• Strong understanding of data modeling and point-in-time correctness
• Experience working with event-driven or clickstream datasets at very large scale
• Experience supporting business-critical production pipelines
• Upper-Intermediate English level or higher

WILL BE A PLUS

• Experience with GCP services including Dataflow, Pub/Sub, GCS, and Beam
• Experience building streaming or near-real-time ingestion systems
• Understanding of feature stores, train/serve skew, and label leakage prevention
• Experience in AdTech or auction-based environments
• Experience handling delayed or incomplete labels in ML systems
• Experience with dbt or similar transformation frameworks
• Experience delivering solutions into Customer-owned infrastructure
• Knowledge of GDPR/CCPA-related privacy engineering practices
• Experience with experimentation infrastructure and statistical validation pipelines
• Experience working in hybrid cloud/on-prem Linux environments
• Terraform and Kubernetes experience
• Experience optimizing warehouse cost and performance

PERSONAL PROFILE

• Strong analytical and problem-solving skills
• Ownership-oriented mindset
• Ability to work independently in a client-facing environment
• Strong communication and documentation skills
• Comfortable working in a fast-paced engineering environment
• Collaborative and proactive attitude