Senior Analytic Engineer

What’s the role?We are looking for an experienced data professional to join our high-performing team that is transforming the future of renting, home ownership, and rewards. You will play a key role driving business growth by building the data foundation to unlock strategic & analytical insights, with an emphasis on structuring, modeling, testing and activating our data. You will take significant ownership of our BigQuery data warehouse, data pipelines from source systems (CDC, file ingestion, streaming replication), and the orchestration and observability frameworks that keep it all running. This is a “full data stack” role — you’ll be equally comfortable writing dbt models and debugging a Datastream CDC pipeline or working with streaming replication to support real-time data products. The ideal candidate has a proven track record developing data products, building reliable data infrastructure, and cultivating key stakeholder relationships.
In this role, you will:

• Be instrumental in growing Bilt’s data function to support the business’ goals
• Partner with other members of the Data team and business stakeholders to model data into usable and scalable formats to drive embedded and self-service analytics
• Lead the Data team with end-to-end development of models/alerts/semantic models/metrics to be consumed by stakeholders and partners
• Identify and execute on opportunities for employing advanced analytics and building complex models to answer business problems such as sessionization of events data, marketing attribution, granular profit modeling, LTV and Churn
• Identify and execute on cost and performance optimizations for existing models, including advanced incremental loading, indexing, partitioning, and clustering strategies
• Build strong relationships with engineering and business stakeholders and serve as a key centralized function to empower data-driven use cases
• Continue advancing our dbt implementation and take ownership of core BigQuery data assets
• Build technical integrations together with engineering stakeholders to scale and power Bilt’s overall 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 — ensuring data is fresh, accurate, and well-monitored
• Roll up your sleeves to provide analytics support when needed — building dashboards in Sigma, performing complex analyses, and partnering directly with business stakeholders to deliver insights, and promoting use of self-service AI analytics tools

In terms of qualifications, we’re seeking: 

• About you:
  • A data professional yearning to solve big-picture problems, learn new things, seek answers in data, who finds comfort in uncertainty 
  • A strong communicator, who thinks in terms of solutions instead of tools, and can clearly explain sophisticated systems to technical and non-technical audiences
  • Engineering-minded with a strong bias towards action, delivering results quickly with iteration instead of waiting for perfection
  • Strong prioritization and organizational skills the ability to juggle many tasks at once in a fast-paced, entrepreneurial environment
  • Experience implementing advanced alerting and monitoring and building resilient systems and processes
  • A strong bias towards being AI-forward, leveraging AI coding assistants (Cursor, Claude Code, Devin) and MCP servers (dbt, BigQuery) to automate processes and create leverage for the business
  • A heart-first contributor who is able to deliver complex projects with multiple stakeholders
• Experience:
  • 6+ years of experience in analytics engineering or data engineering
  • SQL is your primary language, you live and breathe it. Deep fluency in a modern data warehouse (BigQuery strongly preferred). Familiarity with Terraform is ideal; Java/Python familiarity is a nice to have
  • Experience with real-time streaming databases (e.g. Materialize or similar) and event streams (Kafka, Pub/Sub) — you will own PostgreSQL replication into Materialize and help maintain streaming analytics workloads
  • Experience with highly performant analytical databases (e.g. Clickhouse, AlloyDB) and/or caching layers (e.g. Redis) also a plus
  • Strong experience with dbt, comfortable owning / building out dbt projects, leveraging Jinja, YAML and semantic modeling to enable analytics at scale
  • Experience acting as lead for all things data warehouse, including permissions, data governance, scalability and reliability
  • Strong experience orchestrating large datasets and DAG dependencies using dbt Cloud, Airflow, Cloud Composer, or similar tools