Data Analytics Engineer

ASSA ABLOY•Phoenix, AZ

1d•Onsite

About The Position

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.

Requirements

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.
Proficiency in MS Office
Proficiency in word processing, spreadsheets, email and order processing software
Strong relational database knowledge is a must. MS SQL Server 2008/2012/2014 development.
MS Analysis Services development
Knowledge of SSIS, store procedures, triggers, and performance tuning.
Strong knowledge and experience in the Software Development Life Cycle.
Ability to write reports and business correspondence in English and effectively present information and respond to questions from groups of managers, clients, customers, technicians, and assemblers in English.

Nice To Haves

A background experience on SAP Business Objects, QlikView or Cognos, as well as knowledge of SharePoint, Access and JDE Enterprise One 9.0 is highly desirable.
Understanding of reporting tools: SAP Business Objects XI, Cognos, QlikView, etc.
SCRUM experience and certification is a plus.

Responsibilities

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.
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.
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.
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).
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.