Senior Data Engineer - Remote

The company is building the next generation of utility billing. The goal is simple: make a complex, manual, and fragmented process feel seamless, transparent, and intelligent.

The company’s intelligence platform turns raw utility and billing data into actionable intelligence and regulatory peace of mind for multifamily property operators. The platform is past the greenfield phase: foundational modeling is underway, the stack is chosen, and the roadmap is set. They’re hiring their second dedicated data engineer to partner with their existing data engineer and help move from foundation to scale.

What you’ll work on

• Reporting and 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, and retrieval (SQL, RAG, vector, or graph-based)
• Dimensional modeling at the core: build robust facts and dimensions that power analysis for the team and its 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

The stack

• Warehouse: MotherDuck / DuckDB
• Orchestration: Dagster
• Transformation: DBT
• Languages: Python, SQL
• Cloud: Azure
• Adjacent: Hex, MCP, * Collaborative builder: you turn vague requirements into concrete solutions by asking good questions, not guessing
• Product-minded: you understand what you’re building, its impact on customers, and how it fits the business
• Comfortable with ambiguity: the roadmap shifts, and you can prioritize on incomplete information
• Ownership mindset: you treat the platform as a product, monitoring it and thinking ahead
• Quality advocate: tests, observability, and lineage are features, not overhead
• Curious about tooling: you’ve watched the modern data stack evolve and have opinions on DuckDB, Dagster vs. Airflow, and where LLMs do and don’t belong in pipelines
• No task too small: small team, lots of surface area; you’ll occasionally build a quick report or debug someone else’s pipeline

• 4+ years in data platform engineering; bonus if you’ve been a primary builder on a platform from its early stages
• Strong Python and SQL
• Hands-on DBT experience
• Dimensional modeling fluency: star schemas, facts, and dimensions
• Direct experience with the 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

Bonus points

• AI/LLM-adjacent data work: RAG pipelines, embedding stores, evaluation frameworks (LangSmith, PydanticAI), or knowledge-graph approaches to structured retrieval
• Azure experience
• Utility, energy, PropTech, or billing domain background
• Experience building data products for external customers, not just internal BI