About This Role
This engineering role focuses on defining and delivering high-quality data assets that power internal reporting, audit compliance, AI-driven forecasting, and intelligent business operations. It sits at the intersection of business needs and technical solutions, evolving internal tools to be intuitive and innovative. This is an emerging function at Apple, offering the opportunity to define the role within a complex finance environment.
Responsibilities
- Define the vision, strategy, and roadmap for finance data products aligned with business priorities such as close acceleration, forecast accuracy, and regulatory reporting
- Identify and prioritize use cases based on value, risk, and feasibility
- Establish clear product definitions including consumers, SLAs, and success metrics
- Own the lifecycle of data products from ideation through adoption and eventual deprecation
- Partner closely with finance stakeholders across FP&A, accounting, treasury, and tax to understand business processes and data needs
- Translate financial concepts such as revenue recognition, cost allocations, and hierarchies into scalable, governed data models
- Ensure alignment with financial controls, audit requirements, and regulatory standards including SOX and GAAP/IFRS
- Collaborate with architects and engineers to design canonical data models, define data contracts, and establish semantic layers that standardize key financial metrics and dimensions
- Ensure consistency and interoperability of data products across platforms such as Snowflake and Databricks
- Define how curated data is exposed through transformation layers like dbt and semantic tools such as Cube or AtScale
- Work closely with data engineering teams to translate product requirements into technical specifications and deliverables
- Participate in agile processes including backlog prioritization and sprint planning
- Ensure that data pipelines and transformations are scalable, performant, and aligned with enterprise architecture standards within modern data stacks
- Define and enforce data quality rules, validation checks, and certification tiers to ensure trust in finance data products
- Partner with governance teams using tools such as DataHub or Collibra to enable metadata management, lineage tracking, and discoverability
- Ensure data products are well-documented, monitored, and compliant with governance standards
- Communicate product vision, roadmap, and progress clearly to both technical and business stakeholders
- Drive adoption through enablement, documentation, and feedback loops
- Measure product success using KPIs such as adoption, data quality, and measurable business impact
Requirements
