Senior Data Engineer

About the Role

MRCOOL® designs innovative, energy-efficient HVAC systems, including our DIY-friendly ductless mini splits and ducted central air solutions, built to last. As a fast-growing leader in the HVAC industry, our technology and data infrastructure are critical to scaling the business.

MRCOOL is building a new enterprise data warehouse using Snowflake and Google Cloud and we are looking for a Senior Data Engineer to architect, build, and govern it. Our data is spread across more than a dozen platforms — ERP, accounting, CRM, e-commerce, EDI, support, and telephony systems — with no unified source of truth. You will own the greenfield design of a modern Snowflake-based data platform that turns that fragmented data into governed, trusted, decision-ready information.

This is a high-visibility, high-ownership role for an engineer who has done this before. You will design the warehouse, clean up and consolidate years of historical data, establish data governance and a shared business dictionary, and partner with finance and operations leaders to deliver the KPIs, dashboards, and scorecards that drive the business. You will also lay the foundation for AI-driven, natural-language access to our data.

Job Responsibilities - Data Warehouse Architecture & Engineering

• Architecture: Design and build MRCOOL’s new enterprise data warehouse on Snowflake and Google Cloud using modern ELT patterns (raw → staging → conformed core → department marts).
• Pipelines: Build and maintain reliable, scalable data pipelines and ETL/ELT processes to ingest data from NetSuite, Sage Intacct, QuickBooks, HubSpot, Magento, Zendesk, Aircall, Google Analytics/Drive, and other sources.
• EDI integration: Integrate EDI and 3PL/trading-partner data flows (Celigo, SPS Commerce, CommerceHub, Logicbroker, qStock) into the warehouse, working with existing middleware where appropriate.
• Cost & performance: Optimize Snowflake for performance and cost — warehouse sizing, auto-suspend, clustering, and consumption monitoring.

Data Governance & Business Dictionary

• Governance: Establish data governance practices: ownership, access controls, data quality standards, lineage, and PII handling.
• Business dictionary: Create and maintain a business data dictionary and semantic/metrics layer so that key terms (revenue, margin, fill rate, DSO, etc.) have a single, agreed definition across the company.
• Source reconciliation: Reconcile multiple systems of record — including three accounting systems — into a unified chart of accounts and conformed dimensions.

Historical Data Cleanup

• Cleanup: Assess, clean, de-duplicate, and consolidate years of historical data across legacy systems and spreadsheets into the warehouse.
• Quality: Define and implement data quality rules, validation, and reconciliation processes to ensure trust in the data.

KPIs, Dashboards & Scorecards

• Delivery: Partner with finance and operations leadership to define KPIs and build governed dashboards and executive scorecards (starting with Finance as Phase 1).
• BI: Develop reporting in the company’s BI layer (e.g., Power BI / Sigma) on top of governed warehouse marts.

AI & Modern Tooling

• AI enablement: Lay the foundation for natural-language querying against the warehouse (e.g., Snowflake Cortex Analyst or equivalent), ensuring the underlying semantic model is clean and governed.
• Modern tools: Apply the latest data engineering and AI tools to accelerate development, documentation, testing, and data quality.

Required Qualifications

• 10 years of hands-on data warehouse experience, including end-to-end design and delivery.
• 5 years of Snowflake experience required — demonstrated production experience designing and operating Snowflake environments.
• 5 years of Google Cloud experience required.
• Strong EDI experience, including trading-partner / 3PL data integration.
• Strong data pipeline and ETL/ELT experience building reliable, production-grade ingestion and transformation.
• Proven experience with data governance, data quality, and establishing business/metric definitions or a data dictionary.
• Demonstrated experience cleaning up and consolidating historical data from disparate legacy systems.
• Experience building KPIs, dashboards, and scorecards in a modern BI tool (Power BI, Sigma, Tableau, or similar).
• Experience with the latest data and AI tooling, including AI-assisted development and natural-language-to-SQL or LLM-based analytics.
• Advanced SQL and strong data modeling skills (dimensional / conformed modeling).
• Excellent communication skills and the ability to partner directly with finance and operations stakeholders.

Preferred Qualifications

• Experience integrating NetSuite, Sage Intacct, and/or QuickBooks data.
• Experience with dbt or comparable transformation frameworks.
• Experience with EL tools such as Airbyte or Fivetran.
• Experience in distribution, wholesale, manufacturing, or supply-chain environments.
• Familiarity with e-commerce (Magento), CRM (HubSpot), and support/telephony (Zendesk, Aircall) data.
• Bachelor’s degree in Computer Science, Information Systems, or a related field.

Pay: From $130,000.00 per year

Benefits:

• 401(k)
• 401(k) matching
• Dental insurance
• Health insurance
• Life insurance
• Paid time off
• Retirement plan

Experience:

• Snowflake: 5 years (Required)
• Data Warehouse: 8 years (Required)

Work Location: In person

Salary : $130,000