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HilbertHilbertSan Francisco, CA

Forward Deployed Data Engineer (Integration)

Forward Deployed Data Engineer building hybrid data pipelines and semantic layers for Hilbert's AI Growth Engine. Implements warehouse-native or managed ClickHouse integrations, partners with AI agents for accelerated onboarding, and ensures reasoning consistency across customer environments.

Salary not listed
HybridData Engineering

About the role

Responsibilities

  • Own the technical lifecycle of new customers, choosing and implementing the best deployment path (Managed Clickhouse vs. Warehouse-Native).
  • Use Hilbert internal Discovery Agent to create reports and suggest mappings, moving from raw data to a working v1 pipeline in record time.
  • Architect the semantic definitions for custom enterprise data, ensuring our agentic conversation engine has the "Ground Truth" for every query.
  • Transform diverse source data into Hilbert unified growth models to power our generic ML systems.
  • Act as the lead technical resource for high-stakes enterprise implementations, ensuring our stack is "packaged" and performant in their specific infra.
  • Partner with the Data Discovery Agent to analyze customer data, suggest mapping alternatives, and generate the first version of pipelines automatically.
  • Define metadata and business logic so our agentic flows can understand custom columns without hallucination.
  • Ensure "Reasoning Consistency" so AI produces the same high-quality insights regardless of where the data resides.

Requirements

  • Equally comfortable optimizing a Clickhouse query as writing native Snowpark (Snowflake) or BigQuery SQL.
  • Comfortable implementing data orchestration scripts using Python.
  • Understand that an AI needs context beyond just a table; discipline to define the "meaning" behind the data.
  • Able to earn technical trust with a customer's Data Architect quickly, extracting the logic of custom tables and mapping them to Hilbert reasoning engine.
  • Excited to use and improve AI agents that handle data discovery and pipeline scaffolding.

Nice-to-Haves

  • Deep experience with dbt for warehouse-native modeling.
  • Worked with more than one state-of-the-art data warehouse solution and knowing the optimization strategies.
  • Experience with Semantic Layer frameworks (Cube, MetricQL, etc.).
  • Background in E-commerce/Retail (understanding Revenue Metrics, Order lifecycle, LTV, CAC, and Attribution etc.).
  • Having built an agentic workflow before.

Skills

ClickHouseSnowflakeBigQueryPythonSQLSnowparkDagsterAirbytedbtSemantic LayerCubeMetricql

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