Analytics Engineer

We are seeking an Analytics Engineer to join our Corporate Data & Analytics team. This role combines business partnership, analytics development, and data engineering to deliver scalable, governed data solutions across the organization. These solutions will support enterprise analytics, governed self-service, and emerging AI-enabled experiences.

The Analytics Engineer will work closely with business stakeholders to understand operational challenges, gather and document requirements, define meaningful KPIs, and translate those needs into reliable data products. This individual will design, develop, and maintain end-to-end solutions within Microsoft Fabric, including pipelines, notebooks, lakehouses, warehouses, data models, and semantic models. Power BI will remain an important delivery channel, while the broader focus is creating trusted, reusable data products that can support reporting, self-service analytics, AI assistants, agents, APIs, and future business applications.

The ideal candidate can move comfortably between business conversations and hands-on technical delivery, explain complex concepts clearly, and build solutions that are accurate, maintainable, and easy for the business to use.

Key Responsibilities

Business Partnership & Solution Design

• Partner with business leaders, subject-matter experts, and end users to understand business processes, challenges, goals, and reporting needs.
• Lead discovery and requirements gathering sessions; document business rules, data definitions, use cases, acceptance criteria, and success measures.
• Translate business needs into scalable data solutions, including curated data products, semantic models, self-service datasets, dashboards, AI-ready data assets, and operational reporting.
• Advise stakeholders on KPI design, metric consistency, solution options, and when reporting, self-service, automation, or AI-enabled experience best fit the business need.
• Communicate solution options, tradeoffs, progress, and risks clearly to both technical and non-technical stakeholders.

Data Products, Semantic Models & Analytics Experiences

• Design and build reusable data products and semantic models that support Power BI reports, dashboards, scorecards, self-service analytics, and future AI-enabled experiences.
• Develop and maintain reusable semantic models, relationships, hierarchies, calculations, and business logic.
• Create and optimize DAX measures with a focus on accuracy, performance, consistency, and maintainability.
• Partner with business owners to validate KPIs, reconcile results, and establish trusted data assets.
• Enable governed self-service analytics and AI readiness through certified data assets, business-friendly semantic models, clear documentation, and user education.

Microsoft Fabric & Data Engineering

• Design, build, and support data pipelines, notebooks, lakehouses, warehouses, and related components within Microsoft Fabric.
• Develop ingestion and transformation processes using SQL, Python, Fabric Data Factory pipelines, and other tools.
• Build and maintain Bronze, Silver, and Gold data layers using reusable, domain-aligned patterns that support reporting, self-service analytics, and trusted AI consumption.
• Integrate data from enterprise applications, APIs, files, cloud platforms, and on-premises systems.
• Implement monitoring, validation, error handling, and performance improvements to support dependable production solutions.

Data Modeling, Quality & Governance

• Design dimensional models, star schemas, curated data marts, and analytical models that support scalable enterprise reporting.
• Profile and validate source data; identify quality issues and work with business and system owners to resolve them.
• Document data lineage, transformation logic, KPI definitions, solution architecture, dependencies, support procedures, and intended consumption across reports, self-service, and AI-enabled solutions.
• Follow and help strengthen standards for naming, security, access, testing, deployment, and lifecycle management.
• Promote reuse, consistency, business ownership, and responsible use of data across the organization.

Delivery, Support & Continuous Improvement

• Own solutions through discovery, design, development, testing, deployment, adoption, and ongoing maintenance.
• Work within source control and CI/CD practices to deliver reliable, traceable changes across data solutions.
• Troubleshoot data, model, refresh, and report issues and perform root-cause analysis.
• Identify opportunities to reduce manual reporting, retire duplicate solutions, expand self-service, and apply AI or automation where it provides practical business value.
• Share knowledge with teammates and business users through documentation, demonstrations, and working sessions.

Qualifications

Required

• A Bachelor’s degree in Information Systems, Computer Science, Data Analytics, Engineering, Business Analytics, or a related field, or equivalent practical experience.
• Professional experience in analytics engineering, business intelligence, data engineering, or a similar field.
• Demonstrated ability to gather requirements, understand business processes, and translate needs into technical solutions.
• Strong experience developing enterprise data and analytics solutions, including semantic models, DAX, and Power BI delivery.
• Strong SQL skills and experience working with relational and analytical data.
• Experience building or supporting ETL/ELT processes, data pipelines, and data warehousing.
• Knowledge of dimensional modeling, star schemas, data warehousing, and data quality.
• Ability to communicate effectively with business stakeholders, technical teams, and cross-functional partners.
• Ability to manage multiple priorities, work independently, and take ownership of projects.

Preferred

• Hands-on experience with Microsoft Fabric, including Data Factory pipelines, Lakehouse, Warehouse, notebooks, and semantic models.
• Experience developing data transformations or automation using Python.
• Experience with Git, Azure DevOps, deployment pipelines, testing, and CI/CD.
• Experience integrating data from REST APIs, SaaS platforms, SQL Server, and cloud-based systems.
• Experience with data governance, lineage, metadata, access controls, KPI or data-dictionary management, and preparing governed data for self-service or AI use cases.
• Experience supporting enterprise functions such as Finance, Sales, Project Management, Human Resources, Health & Safety, or Operations.
• Experience in the architecture, engineering, construction, environmental, or professional-services industries.
• Familiarity with project-based business metrics such as backlog, utilization, revenue, profitability, project performance, and resource planning.

Core Competencies

• Business partnership: Builds trust, asks effective questions, and converts business needs into clear technical solutions.
• Technical delivery: Builds complete, supportable data solutions across engineering, modeling, semantic layers, visualization, and AI-ready consumption.
• Analytical thinking: Investigates data, validates assumptions, and communicates meaningful insights.
• Quality and ownership: Takes responsibility for accuracy, documentation, maintainability, and user adoption.
• Communication and collaboration: Explains complex information clearly and works effectively across business functions, IT, vendors, and Data & Analytics teammates.
• Continuous improvement: Looks for better patterns, automation opportunities, and reusable solutions.

Technology Environment

• Primary platform: Microsoft Fabric, with Microsoft Power BI as a key analytics and reporting tool.
• Languages: SQL, DAX, and Python.
• Key capabilities: Fabric Data Factory, pipelines, notebooks, Lakehouse, Warehouse, semantic models, Power BI, APIs, Git, Azure DevOps, governed self-service, and AI-ready data products.

Why Join Us

This is an opportunity to help Apex build a modern, governed data and analytics capability while working directly with business teams on meaningful operational and strategic needs. You will help shape how trusted data products are designed, delivered, adopted, and reused across Power BI, self-service analytics, automation, and carefully governed AI-enabled solutions.