Senior Data Analytics Engineer

ASSA ABLOY•Phoenix, AZ

2d•Onsite

About The Position

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.

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 (e.g., a data engineer or analyst) is required — this role has a formal direct report.
Expert SQL + strong data modeling (facts/dimensions; performance-aware).
Proven ability to create reusable analytics assets (certified datasets, metric definitions, semantic consistency) that generalize across business domains, not just one function.
Strong business acumen and ability to proactively propose analyses (not just take requirements).
Exposure to supply chain, manufacturing, or quality analytics is a plus but not required — Sales & Finance domain depth is the priority for this hire; other domains will be learned on the job as scope expands.
Working knowledge of Python for data automation, scripting, and analysis is a plus.
Deep statistical or machine learning expertise is not required for this role.
Comfort applying AI-assisted techniques (e.g., anomaly detection, driver analysis, AI-assisted query/code generation) to accelerate analytics work is a plus — willingness to learn is sufficient; deep AI/ML expertise is not required.
Proficiency in MS Office.
Strong relational database knowledge is a must, including hands-on experience with MS SQL Server and dimensional/star-schema modeling — the majority of source data across functional domains resides in SQL-based systems.
Knowledge of SSIS, stored procedures, triggers, and performance tuning.
Strong knowledge and experience in the Software Development Life Cycle; SCRUM experience and certification is a plus.
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

Experience with Power BI and Analysis Services development (measures, semantic models, DAX) strongly preferred.
Experience with Microsoft Fabric (Lakehouse, Data Pipelines, OneLake) and/or Azure Data Factory for data ingestion and transformation strongly preferred.
Microsoft Certified: Fabric Analytics Engineer Associate or equivalent is a plus.
Candidates without direct Fabric experience but with strong dimensional modeling and cloud data platform fundamentals (e.g., Snowflake, Databricks) are encouraged to apply — Fabric-specific tooling can be learned on the job.
Familiarity with legacy enterprise BI tools (SAP Business Objects, Cognos, QlikView) is a plus for supporting existing reporting during migration, but is not a primary requirement.
Experience with JD Edwards (JDE) Enterprise One or similar ERP-sourced reporting environments is highly desirable, particularly for future Supply Chain and Manufacturing analytics work.

Responsibilities

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.
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.
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.
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.
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).
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 analytics environment, including enterprise-grade AI tooling already in use across EMG IT, governed under our Group Responsible AI Policy (accountability, fairness, reliability, transparency).