Design scalable, cloud-native data architectures in BigQuery for performance and cost efficiency
Build, automate, and maintain ETL/ELT pipelines using modern data tools
Create and optimize data marts and analytical datasets for reporting and insights
Implement data quality, monitoring, logging, and alerts to ensure data reliability
Improve performance, scalability, and cost efficiency of production data infra
Partner with Data Science to build data services and pipelines for ML/analytics
Collaborate with product/analytics/engineering to deliver scalable data solutions

3+ years as a Data Engineer with large-scale data platforms
Strong expertise in data modeling and data warehousing concepts
Proven ETL/ELT design and development experience following BI practices
Hands-on BigQuery: partitioning, clustering, query optimization, cost management
Strong SQL skills; optimize queries in Google SQL
Proficiency in Python and Pandas/PySpark
Experience with ClickHouse: architecture, performance optimization, migrations
GCP knowledge: Cloud Storage, Pub/Sub, Dataform, IAM
Git, CI/CD, automated deployments, and environment management for data workflows
Monitoring, logging, data validation, and production-grade data quality practices
Analytical thinking, problem solving, and ability to work independently
Strong communication with technical and non-technical stakeholders
Understanding of product metrics, experimentation frameworks, and data-driven decisions
2+ years of software engineering experience
Experience optimizing storage and compute to reduce costs
Experience integrating data platforms with BI tools like Tableau or Looker
Familiarity with ML workflows for Data Science teams
Experience with AI-assisted tools like Claude Code/Claude Agents

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