AIML Data Engineer

Summary

Are you passionate about building the data infrastructure that powers AI-driven customer experience measurement — and using that data to uncover the "why" behind the numbers? The AppleCare Customer Insights (ACCI) team is redefining how Apple measures and improves generative support experiences. Our AIML initiatives use large language models to evaluate support conversations across multiple quality dimensions, providing real-time signal to leadership on how our AI-powered support is performing. We are seeking an AIML Data Engineer to own the data pipelines, feature engineering, and telemetry infrastructure that underpin our AIML portfolio — while also serving as a hands-on analytics partner who conducts root cause analysis, targeted investigations, and data-driven deep dives that translate pipeline outputs into actionable insights for program managers and leadership.

Description

The AIML Data Engineer builds and maintains the data foundation that powers ACCI's AI/ML initiatives, and turns that foundation into insight. You will design and implement pipelines that ingest support conversation data, transform it into model-ready formats, orchestrate scoring workflows, and deliver telemetry — then go a step further by partnering with program managers to investigate trends, diagnose performance shifts, and surface the stories in the data that drive decisions. This is a full-stack engineering-and-analytics role that consolidates data pipeline orchestration, model feature engineering, telemetry analytics, and investigative analysis into a single high-impact position.

Responsibilities

• Data Engineering & Infrastructure:
• Design, build, and maintain scalable data pipelines that ingest, transform, and deliver support interaction data to LLM-based scoring systems
• Engineer features and data transformations that prepare conversation data for AI consumption — metadata enrichment, schema normalization, and prompt context assembly
• Build and maintain telemetry pipelines that track model performance, scoring accuracy, and concept drift across LLM-based auto evaluation dimensions
• Develop and operate pipeline orchestration workflows ensuring reliable, timely data delivery across multiple AIML workstreams
• Implement data quality checks, validation, and monitoring at pipeline ingestion points
• Build monitoring and alerting for pipeline health, data freshness, scoring latency, and load failures
• Integrate with upstream data sources (Snowflake, enterprise support systems) and downstream consumers (dashboards, executive reporting, model retraining)
• Analytics & Investigation:
• Conduct root cause analysis when CXI scores, CSAT, or other metrics shift — diagnosing whether changes are data-driven, model-driven, or reflect real customer experience changes
• Partner with program managers on targeted investigations: identifying cohorts, isolating variables, and quantifying impact of specific support experiences
• Proactively surface anomalies, trends, and opportunities from pipeline telemetry before they become escalations

Minimum Qualifications

• Bachelor's degree in Computer Science, Data Science, Statistics, Engineering, or related field (or equivalent experience)
• 4+ years of experience in data engineering or analytics engineering
• Strong proficiency in SQL and Python for both data engineering and analytical investigation
• Experience with cloud data platforms (Snowflake, Databricks, or similar)
• Experience with ETL/ELT tools and pipeline orchestration (dbt, Airflow, Prefect, or similar)
• Demonstrated ability to conduct root cause analysis and translate data findings into actionable recommendations
• Experience with version control (Git) and CI/CD practices

Preferred Qualifications

• Experience building data pipelines supporting LLM-based systems (RAG, scoring, evaluation)
• Experience with data visualization and storytelling (Tableau, Streamlit, or similar)
• Familiarity with NLP data preparation — tokenization, embedding generation, prompt engineering data flows
• Experience with streaming or event-driven data architectures (Kafka or similar)
• Experience with data quality and observability tools (Great Expectations, Monte Carlo, or similar)
• Understanding of concept drift detection and model monitoring pipelines
• Experience supporting program or product teams with investigative analytics in a customer experience domain