The Product Marketing Customer Analytics team is seeking a Finance Data Engineer to design and deliver advanced, scalable customer analytics data pipelines and platforms. The role focuses on building infrastructure optimized for analytics, data analysis, and data science, with an emphasis on data quality, performance, and collaboration with analytics consumers and data scientists.
Location
Austin, TX (onsite)
Compensation
Base pay range: USD 149,200 - 249,000 per year.
Role Summary
This position involves developing batch and streaming capabilities, improving reliability and runtime characteristics, and partnering with analytics stakeholders to address data engineering challenges at scale. The work also includes applying Generative AI techniques, including Retrieval Augmented Generation (RAG), to enhance analytics capabilities.
Responsibilities
- Develop and automate large-scale, high-performance, scalable data platform capabilities for batch and/or streaming to support faster analytics.
- Design large-scale, complex applications and frameworks with strong runtime characteristics, including low latency, fault tolerance, and high availability.
- Build and maintain custom frameworks to support engineering and analytics needs.
- Collaborate with analytic consumers and data scientists to improve new and existing constructs and solve data engineering problems at scale.
- Build data pipelines using Spark, Trino, lakehouse architectures, or similar distributed platforms and Snowflake.
- Implement inclusive data quality checks to help ensure high-quality data.
- Promote strong software engineering practices for building data infrastructure and pipelines at scale.
- Apply structured thinking to break down ambiguous problems and propose impactful solutions.
- Use Generative AI and Retrieval Augmented Generation (RAG) techniques to enhance data analytics capabilities.
- Maintain strong documentation and technical writing, with effective verbal and written communication.
- Bring attention to detail in execution and communication.
Required Qualifications
- 5+ years of relevant Data Engineering experience.
- Undergraduate degree in Computer Science, MIS, Engineering, Mathematics, or another quantitative discipline.
- 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 Kubernetes and Docker is preferred.
- Familiarity with Retrieval Augmented Generation (RAG) techniques working with LLMs.
- Experience creating and consuming Model Context Protocol (MCP) services.
- Experience with Snowflake.
Technologies
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.
- Ability to purchase Apple stock at a discount with voluntary participation in the Employee Stock Purchase Plan.
- Discretionary bonuses or commission payments and relocation (if eligible).
