Senior Data Analytics Engineer

     

 

We’re building a modern analytics practice that goes beyond dashboards. Starting with revenue-focused sales analytics using ERP + non-ERP sources (customer POS, CRM, industry data, spreadsheets, and other structured/unstructured sources), this role will establish reusable analytics foundations (certified datasets, standardized metrics, semantic layer) that reduce ad-hoc reporting and democratize insight generation — with scope expanding over the first year to support Supply Chain, Manufacturing, Quality, and broader Financials analytics as the foundation matures.

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 & Finance revenue analytics and 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) Expansion domains: Supply Chain, Manufacturing & Quality (year-one roadmap)

• As the Sales & Finance analytics foundation matures, extend the same certified-dataset and semantic-layer approach to additional functional domains, sequenced and prioritized jointly with IT and business leadership.
• Supply Chain: inventory, fulfillment, and demand-planning analytics sourced from JDE and related systems.
• Manufacturing: production throughput, downtime, and cost/efficiency analytics.
• Quality: defect and scrap trends, supplier quality performance, and corrective-action tracking, drawing primarily on SQL Server-based operational data alongside other source systems.
• Data across these domains lives in multiple systems, predominantly SQL-based databases — consistent modeling and reconciliation practices across sources will be essential.
• This work begins after Sales & Finance foundations are established; exact scope and sequencing will be set collaboratively based on business priority, not assumed to run in parallel from day one.

C) 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” across the functional domains in scope.
• 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.

D) 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.

E) Contribute to the Analytics Community of Practice

• Contribute to the design of an Analytics COE operating model — one focused on standards, adoption, and scalable enablement rather than report-factory or help-desk patterns.
• Partner with IT leadership to help shape and execute a 12 to 18-month roadmap for analytics capabilities across the domains in scope (platform patterns, data products, priority areas, adoption metrics).

F) Modern tooling & innovation (governed)

• Implement analytics CI/CD patterns (e.g., version control, release discipline, peer review) to scale reliably.
• Apply AI-assisted techniques (e.g., anomaly detection, driver analysis, AI-assisted query or code generation) to accelerate analytics delivery where they improve time-to-insight and adoption.
• Work within an AI-enabled ana