Senior Data Engineer

📋 Description

• Build and scale reliable data infrastructure powering analytics and AI.
• Design and own end-to-end pipelines with dbt on BigQuery.
• Orchestrate data workflows with Dagster for reliability and observability.
• Enable analytics and AI use cases with self-serve datasets.
• Power marketing attribution and reverse ETL to ad platforms.
• Set data platform standards for quality, testing, and deployment.

🎯 Requirements

• Must-haves: Advanced SQL: strong SQL, production pipelines.
• dbt and BigQuery: design, maintain, optimize data workflows.
• Data orchestration: Dagster, Airflow, or Prefect.
• Python: build pipelines and automation with Python.
• Data modeling: dimensional modeling and ELT patterns.
• Git and CI/CD: version control, automated tests, deployment.
• AI mindset: comfortable with AI-driven environment and LLMs.

🎁 Benefits

• Analytics tooling: Hex, Looker, Mode, or Tableau.
• AI-powered data applications: LLM APIs and AI-driven products.
• Advanced Dagster: assets, sensors, partitions, production patterns.
• Real-time data: streaming tech like Pub/Sub, Kafka, Dataflow.
• Data governance: governance, access controls, lineage.
• Marketing data: attribution models, conversion APIs, reverse ETL like Hightouch/Census.