Data Engineer 3

Department Overview

Duties

• Build ingestion from customer satisfaction survey vendors (such as Medallia, Qualtrics, and SMG), in-app surveys, and customer care cases in Salesforce, using vendor APIs and secure file exchange (SFTP, S3 to Cloud Storage, PGP-encrypted files). 
• Integrate social listening data as a guest experience source alongside the survey and customer care data, applying privacy rules to any personal data. 
• Build a reusable vendor ingestion framework that handles rate limits, pagination, retries, incremental loads, and schema changes. 
• Model experience data in BigQuery at restaurant, month, daypart, and channel grain, aligned to restaurant hierarchy master data and linked at aggregate level to guest counts, sales, and speed of service. 
• As vendor verbatim comments become available, prepare free-text feedback for NLP and sentiment models with data scientists, with PII-safe handling of care case notes. 
• Implement PII controls, including masking, tokenization, column-level access, retention, anonymity thresholds, and GDPR access and delete flows, in line with GDPR and CCPA / CPRA. 
• Deliver curated datasets for performance reporting, scorecards, and short-term operational views. 
• Build or extend Python REST services (Flask / FastAPI) that serve curated experience data to restaurant performance applications, and agree API contracts and Data Interface Contracts with the application teams. 
• Develop orchestration with Apache Airflow (Astronomer) and transformations with dbt, using Git, GitHub Actions, automated testing, and vendor-to-warehouse reconciliation. 
• Act as level-2 support for production pipeline incidents, and support market onboarding as survey vendors change. 
• Use approved AI coding assistants (such as GitHub Copilot and Gemini Code Assist) across the lifecycle for code, tests, SQL and dbt models, debugging, and documentation, and review and validate every AI-generated change. 
• Work with the product owner and analysts on survey validity rules and a single satisfaction calculation standard across reporting surfaces. Document pipelines, mentor junior engineers, and coordinate across time zones. 

Qualifications

• 7–11 years of professional experience, with 7+ years of hands-on data engineering. 
• Advanced Python and production-grade SQL. 
• Python REST frameworks (Flask, FastAPI) for building data services. 
• Strong experience integrating third-party and SaaS APIs (REST, OAuth, webhooks) and file-based feeds, including SFTP / S3 file exchange and PGP-encrypted files. 
• Strong GCP data experience: BigQuery, Cloud Storage, Dataflow / Dataproc, Pub/Sub, Cloud Run, and IAM. 
• Hands-on experience with Apache Airflow (Astronomer) and dbt. 
• Experience handling PII and sensitive data with masking, access controls, retention policies, and GDPR access and delete workflows. 
• Practical experience with Docker, Git, GitHub Actions CI/CD, and Terraform. 
• Expert knowledge of data quality functions such as cleansing, standardization, parsing, de-duplication, and mapping. 
• Experience supporting BI users in Tableau. 
• Hands-on experience using AI coding assistants and LLM tools to improve engineering productivity, with sound judgment on code quality and security, and responsible use that keeps confidential and personal data out of prompts. 
• Background in Computer Science, Engineering, or a related field with a bachelor's degree or higher. 
• Preferred: survey and customer care platforms (Medallia, Qualtrics, SMG, Salesforce); NLP pipelines (Vertex AI, Natural Language API); social listening data; Google Professional Data Engineer certification; and quick service restaurant guest experience or delivery domain. 
• Good to know: Java / Spring Boot; MicroStrategy; semantic engineering (semantic layers and governed metric definitions, for example dbt Semantic Layer, and ontology design) and knowledge graphs (Neo4j or Spanner Graph, with Cypher, GQL, or SPARQL).