> Markdown version of [/jobs/ext/206459-americas-business-process-re-engineering-data-engineer](https://www.wearedevelopers.com/jobs/ext/206459-americas-business-process-re-engineering-data-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Americas Business Process Re-Engineering Data Engineer - **Company:** Apple Inc. - **Location:** Austin, TX, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Query Performance, Artificial Intelligence, Airflow, Data Analysis, Apache HTTP Server, Automation of Tests, Code Generation, Code Review, Continuous Integration, Data Architecture, Data Dictionary, Information Engineering, Data Governance, Data Infrastructure, Data Integrity, Extract Transform Load (ETL), Data Visualization, Github, Graph Database, Python (Programming Language), Query Optimization, Cloud Services, DataOps, SQL Databases, Tableau (Software), Web Application Frameworks, Workflow Management Systems, Datadog, Sql Optimization, Large Language Models, Snowflake, Apache Spark, Containerization, Data Lakes, Kubernetes, Information Technology, Data Lineage, Apache Flink, Apache Kafka, Virtual Agents, Streamlit Framework, Artificial Intelligence Markup Language (AIML), Software Version Control, Data Pipelines, Docker - **Published:** May 29, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=084b9066a906ec99 ## About the Role Do you have experience in Tooling?, Do you have a Bachelor's degree?, Ability to work well in a fast-paced, iterative environment and deliver projects under timeline pressures Champion a culture of experimentation and continuous learning, bringing innovative and strategic thinking to reporting, business analytics, and AI-powered automation Exceptional ability to communicate complex data architecture decisions clearly to both technical peers and non-technical senior stakeholders Strong interpersonal and collaboration skills to partner effectively across functions, share knowledge, and integrate diverse feedback Self-sufficient with an ability to thrive in an environment of autonomy amidst ambiguity, with a high bias for action and meticulous attention to data integrity, MS in Computer Science, Data Engineering, Statistics, Applied Math, Data Science, Operations Research or a related field and 8+ years of industry experience OR BS in related field with 10+ years hands-on industry experience Domain expertise in supply chain, operations management, logistics, planning & forecasting, production integration, channel management Demonstrated expertise building and operating large-scale ETL/ELT pipelines using Python, SQL, and modern frameworks (dbt, Spark, Kafka/Flink for streaming) Proficiency with cloud data platforms (e.g. Snowflake) and open table formats (Delta Lake, Apache Iceberg) Strong command of advanced SQL for complex data modeling, query optimization, and analytics engineering Experience with workflow orchestration tools (Apache Airflow or equivalent) and building production-grade, monitored pipelines Hands-on experience implementing data quality frameworks, observability tooling, and data lineage tracking in production environments Experienced with implementation and productionalization of GenAI and Agentic AI tooling including LLM-assisted code generation, MCP servers, and AI-powered data pipeline automation Experience with data visualization and self-service analytics platforms (e.g., Tableau, Streamlit, ThoughtSpot) and the ability to build light front-end data products Track record of staying current with industry best practices, rapidly adopting emerging technologies (e.g., vector databases, RAG pipelines, AI-native data tools), and building functional prototypes to validate concepts ## Description Engage with business and analytics teams to deeply understand data needs and translate requirements into robust, scalable engineering solutions that directly impact Operations decisions Design and implement end-to-end data pipelines and architectures from ingestion and transformation to delivery across batch and real-time streaming workloads Build and maintain high-quality data models (dimensional, relational, or knowledge graph-based) using modern transformation frameworks such as dbt, powering analytics and AIML use cases at scale Architect and operate data workflows using orchestration tools (e.g., Apache Airflow, etc) with built-in monitoring, alerting, and SLA management Implement data observability, lineage tracking, and validation frameworks to uphold data integrity and trustworthiness across the platform Collaborate with Data Scientists, ML Engineers, Software Engineers and Analysts to operationalize models and ensure data infrastructure supports production AIML workflows Partner with infrastructure and platform teams to manage cloud-native data environments (Snowflake, Spark, Delta Lake / Apache Iceberg) with a focus on performance, cost efficiency, and scalability Leverage AI-assisted development tools (e.g., GitHub, Claude) and LLM-powered agents to accelerate pipeline authoring, code review, documentation, and transformation logic generation from natural language specifications Apply DataOps principles including CI/CD pipelines, version control, automated testing, and containerization (Docker, Kubernetes) to deliver reliable, production-grade data products Champion a data product mindset, enabling self-serve analytics and reducing bottlenecks for downstream consumers Tune query performance, partitioning strategies, and storage optimization for data at scale in cloud warehouses and lakehouses Develop and maintain clear technical documentation including data dictionaries, lineage diagrams, and architecture decision records Present data infrastructure capabilities, health metrics, and architectural recommendations to senior leadership in clear, non-technical terms Research and evaluate emerging data engineering technologies including streaming architectures, GenAI-powered data tooling, and next-generation warehousing to expand the team's capabilities and accelerate innovation ## Related Videos - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [Innovating Developer Tools with AI: Insights from GitHub Next](https://www.wearedevelopers.com/videos/1268-innovating-developer-tools-with-ai-insights-from-github-next) - [Debugging in the Dark](https://www.wearedevelopers.com/videos/1658-debugging-in-the-dark) - [Alibaba Big Data and Machine Learning Technology](https://www.wearedevelopers.com/videos/37-alibaba-big-data-and-machine-learning-technology) - [Software Engineering Social Connection: Yubo’s lean approach to scaling an 80M-user infrastructure](https://www.wearedevelopers.com/videos/1583-software-engineering-social-connection-yubo-s-lean-approach-to-scaling-an-80m-user-infrastructure) - [Bringing AI Model Testing and Prompt Management to Your Codebase with GitHub Models](https://www.wearedevelopers.com/videos/1536-bringing-ai-model-testing-and-prompt-management-to-your-codebase-with-github-models) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [What Industries Outside of AI Are Hiring The Most AI Experts?](https://www.wearedevelopers.com/magazine/98-what-industries-outside-of-ai-are-hiring-the-most-ai-experts) - [Dev Digest 120 - Apple and peers](https://www.wearedevelopers.com/magazine/455-dev-digest-120-apple-and-peers)