FDT Enablement Engineer

Summary

Imagine what you could do here. At Apple, new ideas have a way of becoming great products, services, and customer experiences very quickly. Bring passion and curiosity to your job and there's no telling what you could accomplish.

The FDT Enablement Engineer is a strategic advisor and hands-on enabler within our Catalyst Program, focused on driving Finance workforce transformation. This role is pivotal in evolving Finance teams from manual data preparers to AI-enabled decision partners by bridging the gap between business strategy and technology implementation.

Description

Your primary focus will be to guide Finance teams through their maturity journey—from file-based automation to intelligent AI and predictive models—regardless of their current technical expertise. You will empower them to effectively leverage capabilities ranging from data automation and business intelligence to advanced analytics, machine learning, and AI agent creation within a governed, compliant framework.

Responsibilities

• Partner & Strategize: Partner with Finance leaders to assess their team’s current delivery practices and technical maturity across the Finance AI & Data Capabilities maturity spectrum. Guide them in establishing actionable roadmaps for iterative improvement using tools, trainings, and best practices you help to provide.
• Guide & Enable: Develop and deliver tailored roadmaps that outline capability improvements, training needs, and solution pathways in areas including:
  • Data fluency (SQL, data modeling, visualization)
  • Automation and workflow design
  • AI agent creation and management
  • Applied machine learning for forecasting and controls
  • Analytical storytelling and KPI communication
  • Governance and SOX compliance mindset
• Educate & Up-skill: Lead engaging workshops and consultations through programs like the Data Wizard Academy to up-skill Finance teams on practical technical delivery concepts, including:
  • Transitioning from manual or local processes into to governed, automated workflows in Dataiku
  • SQL and Python fundamentals for Finance analysts
  • No-code/low-code entry points with visual recipes
  • Prompt engineering and agentic AI design for Finance use cases
  • Model explainability and human-in-the-loop validation
  • Agile methodologies and responsible innovation
• Collaborate & Build: Work alongside engineering teams and Finance analysts to co-design and prototype accessible solutions such as:
  • Automated workflows connecting to certified data sources in Snowflake/EDW
  • Interactive dashboards with narrative storytelling
  • Finance-specific AI agents for data retrieval, summarization, and task execution
  • Predictive models for forecasting, anomaly detection, and operational efficiency
  • SOX-ready solutions with audit trails and version control
• Champion Governance: Promote and ensure adherence to governance frameworks that enable innovation while maintaining compliance:
  • AI governance frameworks with access controls and audit trails
  • Certified data sources and reusable components
  • Version control and documentation standards
  • “Freedom to fail safely” culture with proper guardrails
  • Making SOX compliance and data security standards easy to understand and follow
• Foster Culture: Act as a learning enthusiast, championing Finance’s evolution from reactive reporting to proactive, predictive decision support. Build a “Finance Analytics Community” for peer support, knowledge sharing, and continuous improvement in data-driven decision-making.

Minimum Qualifications

• 5+ years SQL and Python development experience, including data science and applied machine learning
• Proven background in business analysis, technical consulting, product management, or similar role where you’ve guided technology-driven Finance or business initiatives through transformation
• A rare ability to act as a “translator,” making complex technical concepts (data warehousing, automation, ML, AI agents) accessible and relevant to Finance professionals with varying technical backgrounds
• Demonstrated experience in stakeholder management with Finance leaders, with the ability to influence and build trust at all organizational levels
• Experience in organizational transformation, change management, or digital capability-building programs, particularly moving teams from manual/Excel-based processes to automated, governed workflows
• Understanding of Finance controls, compliance requirements (e.g., SOX), and the need for audit trails in analytical environments

Preferred Qualifications

• Hands-on familiarity with enterprise data platforms like Dataiku, Snowflake/EDW, and data visualization tools (Tableau, Power BI, or similar)
• Experience with no-code/low-code platforms and visual recipe builders that enable business user adoption
• Understanding of AI agent creation, prompt engineering, and LLM fine-tuning for domain-specific applications
• Demonstrated expertise managing code promotion and environment separation, applying clean code architecture principles, developing ETL pipelines, and leveraging Git and automated testing best practices
• Strong knowledge of data governance and PII protection practices, especially in the context of LLM usage and AI-enabled workflows
• Experience building learning programs, academies, or communities of practice
• Experience working within agile or product-based delivery environments
• Familiarity with Finance operations, forecasting, controls, or FP&A processes
• Track record of scaling technical capabilities across large analyst populations

Key skills/competency

• SQL
• Python (Data Science)
• Machine Learning
• AI Agent Creation
• Finance Transformation
• Data Automation
• Stakeholder Management
• Dataiku
• Snowflake
• SOX Compliance