Senior Data Engineer for ETL Pipeline (BigQuery / Snowflake / ClickHouse / dbt / Kafka / PySpark)

We are seeking a Senior Data Engineer to audit, stabilize, and re-architect our geospatial platform powering real-time urban Digital Twins, wildfire mapping, flood simulations, and municipal zone compliance. High-velocity GPS streams, satellite feeds, drone LiDAR clouds, and IoT weather sensors are ingested via Apache Kafka, GCP Pub/Sub, and Debezium CDC into cloud landing zones, but peak traffic regularly causes consumer backpressure, Avro schema drifts, and live map rendering delays. Raw coordinate pings, elevation rasters, and polygon shapefiles sync into Google BigQuery, Snowflake, and ClickHouse, where unpartitioned tables and brute-force spatial joins cause severe latency, disrupt interactive GIS dashboards, and inflate cloud bills by over 50%. Our Airflow and Dagster DAGs execute dbt Cloud transformations and Databricks PySpark pipelines across Medallion layers, but corrupted coordinate geometry continuously breaks dbt runs, corrupts Feast feature stores powering automated risk models, and invalidates Pinecone vector embeddings used for spatial AI analysis. You will audit our full stack, refactor spatial queries using Uber H3 and Google S2 indexing, deploy Great Expectations observability suites, enforce Terraform IaC, and establish automated GitHub Actions CI/CD workflows for zero-downtime delivery. Candidates must have deep mastery of BigQuery, Snowflake, ClickHouse, Apache Kafka, dbt, Databricks, PySpark, Python, SQL, Terraform, Docker, Airflow, Great Expectations, Feast, Pinecone, and complex GIS geospatial telemetry processing.