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Bilt RewardsBilt RewardsNew York, NY

Senior Analytics Engineer

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)

Skills

SQLBigQuerydbtDatastreamMaterializeKafkaPub/SubAirflowCloud ComposerSigmaTableauFivetranAirbyteCensusTerraform
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