Senior Data Engineer

Billee.AIDallas, TX

About The Position

At Billee, we’re building the next generation of utility billing. Our goal is simple: make a complex, manual, and fragmented process feel seamless, transparent, and intelligent. Our intelligence platform turns raw utility and billing data into actionable intelligence and regulatory peace of mind for multifamily property operators. Our platform is past the "greenfield" phase — foundational modeling is underway, our stack is chosen, and the roadmap is set. We're hiring our second dedicated data engineer to partner with our existing data engineer and help us move from foundation to scale.

Requirements

  • 4+ years in data platform engineering.
  • Strong Python and SQL.
  • Hands-on DBT experience.
  • Dimensional modeling fluency. Star schemas, facts and dimensions

Nice To Haves

  • Been a primary builder on a platform from early stages.
  • Direct experience with our stack is a significant plus. In order of preference: Dagster (asset-based orchestration, sensors, partitions) — strongly preferred over Airflow experience alone
  • DuckDB or MotherDuck — even side-project or exploratory use
  • Comfort analyzing data directly.
  • Experience with AI/LLM-adjacent data work: RAG pipelines, embedding stores, evaluation frameworks (e.g. LangSmith or PydanticAI), or knowledge-graph approaches for structured retrieval
  • Azure experience
  • Utility, energy, PropTech, or billing domain background
  • Experience building data products for external customers (not just internal BI)

Responsibilities

  • Reporting & analytics: Contribute to the modeled data and pipelines behind customer-facing reports on consumption, cost, and rate trends.
  • AI-ready data infrastructure: Ingestion, semantic models, storage solutions, and retrieval (SQL, RAG, vector, or graph-based.)
  • Dimensional modeling at the core: Contribute robust facts and dimensions that power analysis for us and for our customers.
  • Platform reliability: Own testing, lineage, freshness monitoring, and alerting so data issues are caught before a customer sees them.
  • Cross-team partnership: Translate vague product and compliance questions into concrete models, working directly with engineers, analysts, and PMs.
  • Build a quick report or debug someone else's pipeline.
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