Note: The job is a remote job and is open to candidates in USA. Reality Labs' Customer Experience team uses customer feedback and insights to drive business improvements. The Data Engineer IV will own scalable data pipelines, data architectures, self-service dashboards, data quality, governance, and AI-driven analytics and automation, while partnering with customer support, operations, product, engineering, and data science teams.
Responsibilities
- Design, develop, integrate, launch, and maintain scalable data pipelines (batch and streaming) that support multiple use cases across PSO
- Build and optimize ETL/ELT workflows, data models, and data warehouse architectures to facilitate efficient development of data artifacts
- Implement data quality frameworks including validation, monitoring, alerting, and lineage tracking to ensure data reliability
- Develop and manage orchestration workflows (e.g., Airflow, Dataswarm) for scheduling and dependency management of data pipelines
- Optimize query performance, pipeline efficiency, and storage costs across large-scale data infrastructure
- Create interactive and dynamic data visualizations that communicate complex insights to stakeholders
- Work with various data sources—including customer interactions, feedback, behavioral data, and operational logs—to integrate and build reports that identify pain points and trends
- Develop and track key performance indicators to measure the effectiveness of customer experience initiatives
- Enable AI/ML-powered analytics by building and maintaining feature pipelines, curated datasets, and model-ready data assets
- Leverage large language models (LLMs) and generative AI tools to automate data workflows, accelerate insight generation, and enhance self-service capabilities for stakeholders
- Develop and maintain prompt engineering frameworks, AI-assisted reporting tools, and intelligent automation solutions that scale team productivity
- Partner with engineering and data science teams to integrate AI/ML model outputs into dashboards and operational workflows
- Evaluate and implement emerging AI tools and techniques to continuously improve the team's analytics and data engineering capabilities
- Work closely with customer service and operations, product, and engineering teams to integrate data insights into business decisions and drive customer experience improvements
- Champion data literacy and AI enablement across the organization through documentation, training, and best practice sharing
Skills
- 6+
- SQL and Python proficiency with strong experience building ETL pipelines at scale
- Airflow or equivalent workflow orchestration tool experience (Apache Spark orchestration)
- Dashboard building experience (Tableau preferred)
- AI experience (e.g., using Claude, AI-generative dashboards, enabling AI capabilities in other organizations)
- 8+ years of experience doing quantitative and operational analyses in a
- 8+ customer support/service, e-commerce, or order management
- 8+ organization
- Strong data engineering skills: experience designing and building production-grade data pipelines, data models, and ETL/ELT processes at scale
- Proficiency in SQL (complex queries, performance tuning, window functions) and at least one programming language (Python preferred)
- Experience with data warehousing platforms (e.g., Hive, Presto, Spark, Snowflake, or BigQuery)
- Experience with workflow orchestration tools (e.g., Airflow, Dataswarm, or equivalent)
- Experience with data visualization tools such as Tableau, Looker, or equivalent for creating self-service dashboards
- Demonstrated experience with data quality frameworks, data governance, and data modeling best practices (dimensional modeling, star/snowflake schemas)
- Hands-on experience with AI/ML enablement—such as building feature pipelines, working with LLM-based tools, or implementing AI-assisted analytics workflows
- Analytics experience manipulating large datasets to formulate insights and drive solutions
- Track record of operating independently, managing ambiguity, and delivering results
- Strong communication skills with experience articulating issues to both technical and non-technical audiences
- Cross-functional experience, including leading or influencing change through data-driven insights
- Experience with generative AI tools and frameworks (e.g., prompt engineering, RAG architectures, LLM APIs, or AI agent workflows)
- Familiarity with version control (Git), CI/CD for data pipelines, and infrastructure-as-code practices
- Experience with streaming data technologies (e.g., Kafka, Spark Streaming)
- Knowledge of metadata management, data cataloging, and data lineage tools
- Background and knowledge of CX/CS operations and metrics
- Familiarity with customer support platforms (e.g., Salesforce)
- Experience with digital analytics tools (e.g., Google Analytics, Adobe Analytics)
- Experience with scripting for automation (Python, Bash) and building internal tooling
- Familiarity with agile development methodologies
- Experience working in the high-volume consumer electronics industry
- Experience working with operations functions, preferably in the customer experience or customer support operations space
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