Apple's US Sales Technology Team is looking for an AI Data Integration Engineer to build the data infrastructure to support Artificial Intelligence and Machine Learning (AIML) initiatives, enhancing sales processes and customer experiences.
Requirements
- 5+ years of experience in designing, building, and maintaining scalable data solutions for large-scale analytics.
- Proficiency in SQL and development experience with cloud database environments like Snowflake, Redshift or Databricks.
- Proficiency in programming languages like Python, Java or R and open-source frameworks for distributed processing like Hadoop and Spark.
- Hands on experience using development tools in a modern cloud data stack for code management, versioning using Git, CI/CD tools, automation and orchestration using Apache Airflow or others and monitoring & alerting.
- Architecting and developing data pipelines through ETL tools, API integrations with systematic and cloud based source systems.
- Strong understanding of data modeling, data warehousing, and ETL concepts (Jupyter Notebooks, Dataiku experience is a plus).
- Experience with Cloud platforms AWS, Azure, Google Cloud.
Responsibilities
- Development and design of data integrations and data ingestion processes for Apple internal and external data.
- Develop data models and mapping rules to transform raw data into actionable insights and reports.
- Design and implement a semantic layer that integrates analytics data from multiple sources in an efficient and effective manner.
- Build and maintain data pipelines for ingesting, processing, and transforming unstructured data sources, such as customer feedback, social media data, or sales call recordings.
- Develop data quality monitoring and validation processes specifically for AIML datasets, including identifying and addressing data bias.
- Work with data scientists to understand data requirements for AIML model training and deployment, ensuring data is available in the appropriate format and quality.
- Implement data governance policies and procedures to ensure the responsible and ethical use of data in AIML applications.
Other
- Eagerness and ability to learn new skills and solve dynamic problems in an encouraging and expansive environment.
- Collaborate with the analytics and data science teams to understand their requirements and deliver solutions that meet their needs.
- Collaborate with internal business partners, internal technology resources (database, system, networking), external vendors, and partners.
- Play an active role in the development and maintenance of user documentation, including data models, mapping rules, and data dictionaries.
- Ability to balance competing priorities, long-term projects, and ad hoc requirements.
