Senior Data Engineer

Yellowstone Local is proud to represent MRCOOL, an industry leader in HVAC.

If you know how to turn fragmented systems into one trusted source of truth, this is your chance to build the data foundation of a fast-growing HVAC brand from the ground up.

• High-visibility, high-ownership role with direct impact on company-wide decision-making
• Opportunity to architect and build a greenfield enterprise data warehouse on Google Big Query and Google Cloud
• Lead the consolidation of data across ERP, accounting, CRM, e-commerce, EDI, support, telephony, and analytics platforms
• Partner directly with finance and operations leadership on business-critical KPIs, dashboards, and executive scorecards
• Work with modern data engineering, AI-assisted development, and natural-language analytics tools
• Shape MRCOOL's data governance, semantic layer, business dictionary, and enterprise reporting standards
• Build the foundation for AI-driven access to trusted business data
• Compensation, schedule, and additional benefits were not specified in the intake

• You will not inherit a finished system and simply maintain it. You will design the architecture, standards, and workflows that define how MRCOOL uses data going forward.
• Your work will solve a meaningful business challenge by bringing more than a dozen disconnected platforms into a governed, decision-ready data environment.
• You will have ownership over warehouse architecture, ELT pipelines, historical data cleanup, governance, data quality, reporting, and AI enablement.
• You will work closely with business leaders instead of operating in a silo, giving you direct visibility into how your engineering decisions affect finance, operations, supply chain, and growth.
• MRCOOL is a fast-growing HVAC technology company focused on innovative, energy-efficient ductless mini splits and ducted central air solutions.
• You will have the opportunity to apply modern Google Cloud, Big Query, BI, data engineering, and generative AI technologies at enterprise scale.

• Serve as the Senior Data Engineer responsible for architecting, building, and governing MRCOOL's enterprise data warehouse on Google Big Query and Google Cloud
• Design a modern ELT architecture using raw, staging, conformed core, and department-level data marts
• Build scalable, production-grade ETL and ELT pipelines that ingest and transform data from Net Suite, Sage Intacct, Quick Books, Hub Spot, Magento, Zendesk, Aircall, Google Analytics, Google Drive, and additional business systems
• Integrate EDI, 3PL, and trading-partner data flows from platforms including Celigo, SPS Commerce, Commerce Hub, Logicbroker, and qStock
• Optimize Big Query performance and cost through partitioning, clustering, storage strategies, materialized views, workload management, slot reservations, editions, and consumption monitoring
• Establish enterprise data governance practices covering ownership, role-based access, lineage, PII handling, metadata, and data quality
• Create and maintain a shared business data dictionary and semantic or metrics layer so core terms such as revenue, margin, fill rate, and DSO have consistent definitions
• Reconcile multiple systems of record into unified financial structures, conformed dimensions, and a consistent chart of accounts
• Assess, clean, deduplicate, validate, and consolidate years of historical data from legacy systems, databases, and spreadsheets
• Define automated data quality rules, reconciliation processes, and validation controls that improve trust in reporting
• Partner with finance and operations leaders to define KPIs and deliver governed dashboards and executive scorecards, beginning with Finance
• Build reporting solutions using modern business intelligence platforms such as Power BI, Looker, Looker Studio, Tableau, or similar tools
• Create the governed data and semantic foundation required for natural-language querying and AI-powered analytics using tools such as Gemini in Big Query, Conversational Analytics Agent/API, or comparable technologies
• Use modern data engineering and AI-assisted development tools to improve testing, documentation, pipeline reliability, metadata management, and developer productivity
• Work with Google Cloud technologies including Big Query, Google Cloud Storage, Dataflow, Dataproc, Cloud Data Fusion, Datastream, Big Query Data Transfer Service, Dataplex, Cloud Functions, Cloud Run, Pub/Sub, and related services
• Job location:
  
  City and state were not provided in the intake