Job Title Sales Data Engineer (Contract) About Jaunty Jaunty is a New York cannabis brand. We run a field-heavy sales model and depend on clear, reliable reporting to guide our strategy and field teams. Role Summary We need a hands-on Analytics & BI Engineer to rebuild our Power BI reporting in Looker Studio and expand our reporting capabilities. The initial effort will be build-heavy. The work then shifts to steady improvements and maintenance. Workload • Phase One: 20 hours per week minimum for the first 30-60 days of the project • Phase Two: 10–20 hours per month for enhancements, fixes, and refresh monitoring. Expected Compensation -Phase 1: Migration & Initial Build Duration: 30–60 days (approx. 1–2 months) Hourly Rate Range: $120–$200/hour • $120/hr: for highly capable but with less specific experience with our platforms • $130–$175/hr: for seasoned data engineers with most of the stack covered (SQL, Looker Studio, CRM experience) • $175+/hr: for candidates with specific experience in cannabis sales ops data, Semantic‑model enrichment for LLM Integrations, Sales Regression Modeling Estimated Cost (for 1.5 months at 20 hrs/week): $14,800 – $24,000 -Phase 2: Ongoing Maintenance & Enhancements Hourly Rate: Same as initial build phase or slightly reduced if candidate is open to retainer. Optional Monthly Retainer Model: • $1,500/month for up to 15 hours • $2,000/month for up to 20 hours • Additional hours billed at negotiated hourly rate -Other Terms & Inclusions • Invoicing: Monthly • Payment Terms: Net 30 • Tools/Software: Contractor will use their own hardware; Jaunty provides access to data sources and environment. • NDA & IP Agreement: Required at contract start • Communication: Asynchronous preferred (Loom updates, email) • Meeting Cadence: Weekly check-ins optional, as needed by project milestones Core Objectives • Recreate essential Power BI views in Looker Studio with equivalent or better logic and usability. • Establish dependable pipelines and refresh schedules across CRM, POS, and market data. • Implement data quality rules, documentation, and auditability. • Deliver rep‑facing dashboards that drive reorder prompts, weekly priorities, and store targeting. • Stand up automated alerts for reorders, new opportunities, and underperformance. Primary Responsibilities • Audit current Power BI assets and translate DAX/business logic to SQL/Looker Studio. • Model data for clarity and reuse. Create clean, documented datasets and fields. • Connect and blend sources, including: • CRM: Outfield. • ERP: Canix now; Acumatica later. • Market/POS: Headset, Hoodie, and retailer POS feeds. • Google Sheets/Forms: marketing spend, field activity. • Public data: NY State licensing; schedule automated ingestion. • Build Version 1.0 dashboards: • Top Line Sales by Territory • Market Penetration by Product Line • Points of Distribution Gaps (Swiss Cheese Reports) by Store and Territory • Total Sales by Product Line • About a dozen more reports • Configure alerts via G Chat/email for key thresholds based on reporting. • Define and enforce data hygiene standards: naming, keys, timestamps, SCD handling, source of truth tables. • Restore historical fidelity where needed (store open dates, activity windows). • Write clear documentation: data dictionary, refresh SLAs, ownership, runbooks. • Partner with Sales & Marketing leadership to refine metrics and acceptance criteria. • Actively communicate progress on the project via email and/or Loom updates, so as to minimize meetings. Required Qualifications • 4+ years in Analytics Engineering or BI Engineering roles. • Advanced Looker Studio experience, including data modeling, blending limits, and performance tuning. • Strong SQL. Comfortable designing warehouse tables and views. • Proven work migrating reports from Power BI to another stack. • Experience supporting Sales Ops/RevOps reporting and CRM data. • Track record implementing automated alerts and scheduled jobs. • Solid versioning and documentation habits. Preferred Qualifications • Outfield CRM experience or similar field-sales CRMs. • Google BigQuery experience as the analytical back end for Looker Studio. • Python for API pulls and lightweight ETL; dbt or equivalent ELT tooling. • Fivetran/Stitch/Airbyte or Make/Zapier experience, with bias toward reliability and cost control. • Experience building rep coaching insights and opportunity flags. • Familiarity with platforms such as Headset, Hoodie, or analogous retail scan/market data. • Familiarity with Acumatica or Canix data structures. Success Metrics • Migration completion and parity with Power BI. • Data freshness meets agreed SLAs and alerting works reliably. • Dashboard load times remain acceptable for field use. • Clear documentation that another analyst can follow. • Fewer manual spreadsheet touches and ad-hoc fixes over time.