Build and own Bilt's full data stack including BigQuery warehouse, dbt models, CDC/streaming pipelines, and observability. Drive analytics, data modeling, and self-service tooling for business stakeholders.
Salary not listed
On-site6+ YOEData Engineering
About the role
Responsibilities
Partner with Data team and business stakeholders to model data into usable and scalable formats for embedded and self-service analytics
Lead end-to-end development of models, alerts, semantic models, and metrics for stakeholders and partners
Identify and execute opportunities for advanced analytics and complex models (sessionization, marketing attribution, profit modeling, LTV, Churn)
Identify and execute cost and performance optimizations for existing models (incremental loading, indexing, partitioning, clustering)
Build strong relationships with engineering and business stakeholders to empower data-driven use cases
Advance dbt implementation and own core BigQuery data assets
Build technical integrations with engineering stakeholders to scale data capabilities
Build and maintain data pipelines from source databases into BigQuery using CDC (Datastream), file ingestion (SFTP/GCS), and streaming replication to Materialize
Own pipeline reliability, orchestration, alerting, and observability across batch and streaming systems
Provide analytics support: build dashboards in Sigma, perform complex analyses, partner with stakeholders to deliver insights, promote self-service AI analytics tools
Requirements
6+ years of experience in analytics engineering or data engineering
Deep fluency in SQL and a modern data warehouse (BigQuery strongly preferred)
Experience with real-time streaming databases (Materialize or similar) and event streams (Kafka, Pub/Sub)
Strong experience with dbt, including owning/building dbt projects, Jinja, YAML, and semantic modeling
Experience acting as lead for data warehouse operations including permissions, data governance, scalability, and reliability
Strong experience orchestrating large datasets and DAG dependencies using dbt Cloud, Airflow, Cloud Composer, or similar tools, including alerting and observability frameworks
Hands-on experience with data ingestion technologies: CDC (Datastream or similar), file-based ingestion (SFTP, GCS), managed connectors (Fivetran, Airbyte)
Experience with BI Tools (Sigma, Omni, Tableau) and building self-service solutions
Experience with reverse ETL solutions (Census, Airbyte)
Nice-to-Haves
Familiarity with Terraform
Java/Python familiarity
Experience with highly performant analytical databases (Clickhouse, AlloyDB) and caching layers (Redis)
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