ASSA ABLOY is building governed analytics foundations that help teams move from reporting to faster, trusted decision-making. In this onsite role in Phoenix, AZ, you will build and scale analytics product capabilities for revenue-focused Sales and Finance, then expand within your first year to Supply Chain, Manufacturing, and Quality analytics. You will enable self-service insight through certified datasets, a standardized metrics and semantic layer, and modern governed tooling in an AI-enabled environment governed under the Group Responsible AI Policy.
Responsibilities
- Partner with Sales and Finance to deliver a differentiated sales analytics product that improves decision-making on revenue drivers including pricing/discounting, mix, customer/segment performance, and channel.
- Create executive-ready insight narratives and repeatable analytic decision frameworks such as driver trees, leading indicators, and KPI hierarchies.
- Integrate and reconcile new data sources beyond ERP, including customer POS feeds, CRM, external or industry signals, customer master enrichment, and spreadsheets, into governed analytical datasets.
- As the Sales & Finance foundation matures, extend the certified-dataset and semantic-layer approach to additional functional domains in a sequence prioritized jointly with IT and business leadership.
- Lead domain analytics expansion across:
- 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 using primarily SQL Server-based operational data alongside other systems.
- Design and own curated analytics datasets and reusable dimensional models that become a single source of truth across the domains in scope.
- Establish and enforce consistent KPI definitions using a metrics/semantic-layer approach so metrics are defined once and reused everywhere.
- Implement testing, documentation, and data-quality practices to drive trust and adoption of analytics outputs.
- Reduce ad-hoc reporting through certified datasets, reusable templates, and clear consumption patterns that support business self-service.
- Provide training and enablement through office hours, best-practice templates, and “how to use” documentation, and help build analytics community rituals.
- Contribute to the design of an Analytics COE operating model centered on standards, adoption, and scalable enablement rather than report-factory or help-desk patterns.
- Partner with IT leadership to shape and execute a 12 to 18-month roadmap for analytics capabilities across the domains in scope, including platform patterns, data products, priority areas, and adoption metrics.
- Implement analytics CI/CD patterns such as version control, release discipline, and peer review to scale reliably.
- Apply AI-assisted techniques, including anomaly detection, driver analysis, and AI-assisted query or code generation, to accelerate time-to-insight and adoption where it improves outcomes.
- Operate within an AI-enabled analytics environment with enterprise-grade AI tooling already used across EMG IT, governed under the Group Responsible AI Policy (accountability, fairness, reliability, transparency).
Requirements
- 8–10+ years in analytics/BI/data roles with evidence of business impact and cross-functional partnership.
- Prior experience directly managing or supervising technical staff is required; this role has a formal direct report.
- Expert SQL and strong data modeling skills, including performance-aware fact/dimension modeling.
- Proven ability to build reusable analytics assets (certified datasets, metric definitions, semantic consistency) that generalize across business domains.
- Strong business acumen and proactive approach to proposing analyses, not only translating requirements.
- Exposure to supply chain, manufacturing, or quality analytics is a plus, with Sales & Finance domain depth prioritized; other domains will be learned as scope expands.
- Working knowledge of Python is a plus.
- Comfort using AI-assisted techniques to accelerate analytics work is a plus; deep AI/ML expertise is not required.
- Proficiency in MS Office.
- Strong relational database knowledge, including hands-on experience with MS SQL Server and dimensional/star-schema modeling since much of the source data resides in SQL-based systems.
- Power BI and Analysis Services development (measures, semantic models, DAX) are strongly preferred.
- Experience with Microsoft Fabric (Lakehouse, Data Pipelines, OneLake) and/or Azure Data Factory for ingestion and transformation is strongly preferred.
- Knowledge of SSIS, stored procedures, triggers, and performance tuning.
- Strong knowledge and experience with the Software Development Life Cycle; SCRUM experience and certification are a plus.
- Ability to write reports and business correspondence in English and effectively present information and respond to questions in English to managers, clients, customers, technicians, and assemblers for business and safety reasons.
Technologies
SQL, Python, MS SQL Server, Power BI, Analysis Services, DAX, Microsoft Fabric (Lakehouse, Data Pipelines, OneLake), Azure Data Factory, SSIS, stored procedures, triggers, Scrum, SAP Business Objects, Cognos, QlikView, JD Edwards (JDE), ERP, AI-assisted techniques, anomaly detection, driver analysis, AI-assisted query or code generation
Physical Demands
- Frequently required to sit, stand, walk, stoop and kneel; use hands, reach with hands and arms; communicate clearly and effectively.
- May be frequently required to lift up to 10 pounds.
Work Environment
- Noise level is moderate to loud in the work and shop environment.
- Occasionally required to work near fumes or airborne particles and toxic or caustic chemicals.
- May be required to work near moving mechanical parts.
