Senior Data Analytics Engineer

ASSA ABLOY Group; Phoenix (AZ); On-site; USD 140,000 - 180,000; Full time

Job summary

ASSA ABLOY Group is seeking an experienced analytics leader to build a modern analytics practice in Phoenix, delivering revenue-focused analytics across Sales, Finance, and expanding to Supply Chain, Manufacturing, and Quality. You will design reusable data products, semantic layer, and standardized metrics to enable self-service insights.

You\'ll lead a team, partner with IT and business leaders, implement data quality, CI/CD analytics, and AI-assisted techniques to accelerate time-to-insight

Qualifications

• 8–10+ years in analytics/BI/data roles with evidence of business impact and cross-functional partnership.
• Experience managing or supervising technical staff (e.g., a data engineer or analyst).
• 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.
• Strong business acumen and ability to proactively propose analyses.
• Exposure to supply chain, manufacturing, or quality analytics is a plus but not required; Sales & Finance domain depth is the priority.
• Working knowledge of Python for data automation, scripting, and analysis is a plus.
• Comfort applying AI-assisted techniques (e.g., anomaly detection, driver analysis, AI-assisted query/code generation) to accelerate 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 (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).
• Extend 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 from JDE and related systems.
• Manufacturing: production throughput, downtime, and cost/efficiency analytics.
• Quality: defect and scrap trends, supplier quality performance, corrective-action tracking (SQL Server-based data).
• Data across domains lives in multiple SQL-based systems — consistent modeling and reconciliation practices across sources will be essential.
• 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 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 analytics outputs.
• Self-service enablement & analytics democratization.
• Reduce ad-hoc reporting by delivering certified datasets, templates, and clear consumption patterns for safe self-service.
• 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 focused on standards, adoption, and scalable enablement.
• Partner with IT leadership to shape a 12–18-month roadmap for analytics capabilities across the domains in scope.
• Modern tooling & innovation (governed).
• Implement analytics CI/CD patterns (version control, release discipline, peer review) to scale reliably.
• Apply AI-assisted techniques (anomaly detection, driver analysis, AI-assisted query/code generation) to accelerate analytics delivery.
• Work within an AI-enabled analytics environment, governed under Group Responsible AI Policy.

Skills

Relational databases

SQL proficiency

SCRUM/Agile

Tools

Power BI

Analysis Services

Microsoft Fabric

Azure Data Factory

Snowflake

Databricks

Job description

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

• 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).

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.
• 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.

What Success Looks Like (6 Months)

• A Sales & Finance revenue analytics capability that integrates non-ERP signals and is actively used by Sales leadership for pricing/revenue decisions.
• Measurable reduction in ad-hoc reporting through certified datasets, templates, and defined intake/triage patterns.
• A well-managed, productive direct report with clear goals and growth plan in place.
• Active contribution to a functioning Analytics Community of Practice with an agreed 12–18-month roadmap and adoption goals.
• A scoped, prioritized plan (not full delivery) for Supply Chain, Manufacturing, and Quality analytics expansion.

Computer Skills

• 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.
• 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.
• Knowledge of SSIS, stored procedures, triggers, and performance tuning.
• 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.
• Strong knowledge and experience in the Software Development Life Cycle; SCRUM experience and certification is a plus.

Language Ability

• For business and safety reasons, 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.

PHYSICAL DEMANDS

Physical demands described are representative of those that must be met by an employee to successfully perform the essential functions of the job. While performing the functions of this position, the employee is frequently required to sit, stand, walk, stoop and kneel; use hands, reach with hands and arms; communicate clearly and effectively. The employee may also be frequently required to lift up to 10 pounds.

WORK ENVIRONMENT

The work environment characteristics described here are representative of those an employee encounters while performing the essential functions of this position. The noise level in the work and shop environment is moderate to loud. While performing the duties of this position the employee may occasionally be required to work near fumes or airborne particles and toxic or caustic chemicals. The employee may also be required to work near moving mechanical parts.

We are the ASSA ABLOY Group

Our people have made us the global leader in access solutions. In return, we open doors for them wherever they go. With nearly 63,000 colleagues in more than 70 different countries, we help billions of people experience a more open world. Our innovations make all sorts of spaces – physical and virtual – safer, more secure, and easier to access. As an employer, we value results – not titles, or backgrounds. We empower our people to build their career around their aspirations and our ambitions – supporting them with regular feedback, training, and development opportunities. Our colleagues think broadly about where they can make the most impact, and we encourage them to grow their role locally, regionally, or even internationally. As we welcome new people on board, it’s important to us to have diverse, inclusive teams, and we value different perspectives and experiences.