Finance Data Engineer

The Product Marketing Customer Analytics team is seeking a Finance Data Engineer to design and deliver scalable customer analytics data pipelines and platforms. The role focuses on analytics-ready infrastructure spanning batch and streaming, with an emphasis on data quality, performance, and close collaboration with analytics consumers and data scientists.

Location

• Austin, TX (onsite)

Compensation

• USD 149,200 - 249,000 per year

Key Responsibilities

• Develop and automate large-scale, high-performance batch and/or streaming platform capabilities to support faster analytics
• Design large-scale, complex application frameworks with strong runtime characteristics, including low latency, fault tolerance, and availability
• Build and maintain custom frameworks aligned to engineering and analytics needs
• Partner with analytics consumers and data scientists to create and improve constructs and address data engineering challenges at scale
• Build data pipelines using Spark, Trino, lakehouse, or comparable distributed platform approaches, including Snowflake
• Deploy inclusive data quality checks to support high-quality datasets
• Promote high standards in software engineering practices for data infrastructure and pipelines built at scale
• Apply structured problem-solving to break down ambiguous issues and propose impactful solutions
• Apply Generative AI and Retrieval Augmented Generation (RAG) techniques to enhance data analytics capabilities
• Maintain strong documentation and technical writing; use effective verbal and written communication
• Demonstrate attention to detail in delivery

Required Qualifications

• 5+ years of relevant Data Engineering experience
• Undergraduate degree in Computer Science, MIS, Engineering, Mathematics, or another quantitative discipline (required)
• 5+ years of experience in data engineering and ETL pipeline development
• 5+ years of experience with Big Data Technologies including Spark, Lakehouse, and Trino
• Experience with Snowflake
• Familiarity with RAG techniques working with LLMs
• Experience creating and consuming Model Context Protocol (MCP) services

Preferred Qualifications

• Experience with Kubernetes and Docker (preferred)
• Additional alignment with the required areas above, including Spark, Lakehouse, Trino, RAG with LLMs, MCP services, and Snowflake

Technical Skills and Tools

• Spark, Trino, Lakehouse, Snowflake
• Kubernetes, Docker
• Generative AI, Retrieval Augmented Generation (RAG), LLMs
• Model Context Protocol (MCP)

Benefits

• Comprehensive medical and dental coverage
• Retirement benefits
• Discounted products and free services
• Reimbursement for certain educational expenses, including tuition
• Opportunity to become an Apple shareholder through Apple’s discretionary employee stock programs
• Eligibility for discretionary restricted stock unit awards
• Option to purchase Apple stock at a discount through the Employee Stock Purchase Plan
• Discretionary bonuses or commission payments and relocation (if eligible)