Data Solution Engineer, Data Quality and Governance

Apple • Cupertino, CA

2h

About The Position

Imagine what you could do here. The people here at Apple don't just create
products — they create the kind of wonder that's revolutionized entire
industries. It's the diversity of those people and their ideas that inspires the
innovation that runs through everything we do, from amazing technology to
industry-leading environmental efforts. Join Apple, and help us leave the world
better than we found it. Apple's Sales organization generates the revenue needed
to fuel our ongoing development of products and services. This in turn, enriches
the lives of hundreds of millions of people around the world. Our sales team is
in many ways, the face of Apple to our largest customers. We are here to grow
Apple's business through partnering with our enterprise, education & channel
sales teams, resellers, partners and carriers. We are seeking a Data Solution
Engineer to lead the design and implementation of enterprise data quality
solutions enhanced by generative AI and machine learning capabilities. This role
bridges traditional data quality disciplines with emerging AI technologies to
create intelligent, self-improving data quality frameworks that scale across
global sales operations. The ideal candidate combines deep expertise in data
quality architecture with hands-on experience applying large language models,
semantic technologies, and automated reasoning to solve complex data challenges.

DESCRIPTION

In this role, you will: Design end-to-end data quality architectures that
integrate GenAI capabilities for automated profiling, anomaly detection, root
cause analysis, and remediation recommendations Develop reference architectures
for AI-augmented data quality platforms, including integration patterns with
enterprise data catalogs, knowledge graphs, and metadata management systems
Create technical blueprints for real-time data quality monitoring using ML-based
pattern recognition and predictive quality scoring Design and implement
LLM-powered solutions for automated data quality rule generation, business
glossary enrichment, and natural language data profiling interfaces Create ML
pipelines for automated data classification, sensitivity detection, and quality
dimension scoring Evaluate and integrate emerging AI technologies (foundation
models, agents, multimodal capabilities) into the data quality technology stack
Lead technical design for data quality platforms built on modern data stack
technologies (DataHub, Great Expectations, dbt, etc.) Design API-first
architectures enabling self-service data quality capabilities for business users
and data product teams Architect observability and lineage solutions providing
end-to-end visibility into data quality across the data lifecycle Partner with
Data Stewards, Data Product Owners, and Business Analysts to translate quality
requirements into technical solutions Collaborate with enterprise architecture,
information security, and infrastructure teams on platform decisions Mentor
engineers and analysts on data quality best practices and AI/ML implementation
patterns Present technical recommendations to senior leadership

Requirements

• Typically requires a minimum of 8+ years of related experience in software, data architecture, data engineering, or data quality roles
• Expert-level knowledge of data quality dimensions, profiling techniques, and remediation patterns
• Strong understanding of master data management, data cataloging, and metadata management principles
• Deep understanding of data modeling, dimensional modeling, and architectural patterns for analytical and operational data stores
• Proficiency in design and development of custom ETL pipelines using SQL and scripting languages (Python, etc.)
• Experience with data pipeline orchestration (Airflow, Dagster, Prefect) and streaming technologies (Kafka, Spark)
• Proficiency in major cloud platforms (AWS, Azure, or GCP) including data services and AI/ML offerings
• Solid understanding and practical application of API design principles (REST, GraphQL) and microservices patterns
• Demonstrated experience with version control systems (Git), implementing CI/CD pipelines, and Infrastructure-as-Code (IaC) practices.
• Ability to thrive in a fast-paced environment while balancing speed, accuracy, and long-term design principles
• Strong communication skills and comfort working with both technical and business stakeholders
• Bachelor's degree in Computer Science, Data Science, Information Systems, or related field

Nice To Haves

• Hands-on experience with LLMs such as GPT-4, Claude, or Llama, including expertise in prompt engineering, fine-tuning, and RAG implementations.
• Proficiency with data visualization and BI tools like Superset or Tableau.
• Strong understanding of authentication and authorization patterns
• Master’s degree in related field preferred

Responsibilities

• Design end-to-end data quality architectures that integrate GenAI capabilities for automated profiling, anomaly detection, root cause analysis, and remediation recommendations
• Develop reference architectures for AI-augmented data quality platforms, including integration patterns with enterprise data catalogs, knowledge graphs, and metadata management systems
• Create technical blueprints for real-time data quality monitoring using ML-based pattern recognition and predictive quality scoring
• Design and implement LLM-powered solutions for automated data quality rule generation, business glossary enrichment, and natural language data profiling interfaces
• Create ML pipelines for automated data classification, sensitivity detection, and quality dimension scoring
• Evaluate and integrate emerging AI technologies (foundation models, agents, multimodal capabilities) into the data quality technology stack
• Lead technical design for data quality platforms built on modern data stack technologies (DataHub, Great Expectations, dbt, etc.)
• Design API-first architectures enabling self-service data quality capabilities for business users and data product teams
• Architect observability and lineage solutions providing end-to-end visibility into data quality across the data lifecycle
• Partner with Data Stewards, Data Product Owners, and Business Analysts to translate quality requirements into technical solutions
• Collaborate with enterprise architecture, information security, and infrastructure teams on platform decisions
• Mentor engineers and analysts on data quality best practices and AI/ML implementation patterns
• Present technical recommendations to senior leadership

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Number of Employees: 5,001-10,000 employees