• Build and extend batch pipelines using dbt for transformations and Dagster for orchestration, scheduling, and asset-driven lineage. • Develop and optimize BigQuery data models (dimensional, wide-table, or domain-oriented) to support analytics, experimentation, and reporting use cases. • Advance real-time streaming capabilities by implementing and maintaining Kafka/PubSub + Flink pipelines, primarily using FlinkSQL, to deliver low-latency datasets and event-derived metrics. • Design data platform standards: SDLC, naming conventions, modeling patterns, incremental strategies, schema evolution approaches, and best practices for batch + streaming including CI/CD and testing. • Improve reliability and observability by implementing monitoring, alerting, and SLAs/SLOs for pipelines and data quality. • Partner with analytics, product, and engineering teams to onboard new data sources, define contracts, and deliver trusted datasets. • Own platform operations including performance tuning, data quality, cost optimization, and scaling across both warehouse and streaming systems. • Design a unified serving layer architecture that cleanly exposes consistent, trusted datasets across both batch and streaming systems. • Establish strong data governance, reliability standards, and observability practices.