Data Analytics Engineer

     

 

We’re building a modern analytics practice that goes beyond dashboards. This role will create revenue-focused sales analytics using ERP + non-ERP sources (customer POS, CRM, industry data, spreadsheets, and other structured/unstructured sources), and will establish reusable analytics foundations (certified datasets, standardized metrics, semantic layer) that reduce ad-hoc reporting and democratize insight generation.

This is an in-office position in Phoenix, Arizona.

 

ESSENTIAL FUNCTIONS & RESPONSIBILITIES 

To perform this job successfully, an individual must be able to perform each essential function satisfactorily:

A) Sales revenue analytics & decision enablement (first 6 months priority)

• Partner with Sales and Finance to build a differentiated sales analytics product that improves decision-making on revenue drivers (e.g., pricing/discounting, mix, customer/segment performance, channel).
• Create executive-ready insight narratives and repeatable analytic “decision frameworks” (driver trees, leading indicators, KPI hierarchies).
• Integrate and reconcile new sources beyond ERP (e.g., customer POS feeds, CRM, external/industry signals, customer master enrichment, spreadsheets) into governed analytical datasets.

B) Analytics engineering: data products, semantic layer, and standardized metrics

• Design and own curated analytics datasets and reusable dimensional models that become a “single source of truth” for Sales and Finance analytics.
• Establish and enforce consistent KPI definitions via a metrics/semantic layer approach (define metrics once, reuse everywhere).
• Implement testing, documentation, and data-quality practices so stakeholders trust and adopt the analytics outputs.

C) Self-service enablement & analytics democratization

• Reduce ad-hoc reporting by delivering certified datasets, reusable templates, and clear consumption patterns that allow business users to self-serve safely.
• Establish training/enablement (office hours, best-practice templates, “how to use” documentation) and analytics community rituals.

D) Lead the Analytics COE / Community of Practice (roadmap ownership)

• Build an Analytics COE operating model that is not a report factory and not a help desk—focused on standards, adoption, and scalable enablement.
• Produce and manage a 12 to 18-month roadmap for analytics capabilities (platform patterns, data products, priority domains, adoption metrics).

E) Modern tooling & innovation (governed)

• Implement analytics CI/CD patterns (e.g., version control, release discipline, peer review) to scale reliably.
• Introduce modern techniques/tools where they improve time-to-insight and adoption, including governed AI-assisted analytics experiences supported by trusted semantic metrics.

 

QUALIFICATIONS

The requirements listed below are representative of the knowledge, skills, and/or abilities required for this position. 

 

Education and/or Experience:

• 8–10+ years in analytics/BI/data roles with evidence of business impact and cross-functional partnership.
• Expert SQL + strong data modeling (facts/dimensions; performance-aware).
• Proven ability to create reusable analytics assets (certified datasets, metric definitions, semantic consistency).
• Strong business acumen and ability to proactively propose analyses (not just take requirements).
• Ability to mentor and lead technically (player/coach) and guide a data engineer.

 

What success looks like (6 months)

1. A Sales revenue analytics capability that integrates non-ERP signals and is actively used by Sales leadership for pricing/revenue decisions.
2. Measurable reduction in ad-hoc reporting through certified datasets, templates, and defined intake/triage patterns.
3. A functioning