Data Engineer IV

Job 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:

• Building and maintaining self-service dashboards for stakeholders across PSO.
• Enabling AI capabilities and integration across the organization to reduce manual effort.
• Performing QA and validation for dashboards to ensure data accuracy and reliability.
• Designing, building, and maintaining data pipelines to move data between systems.
• Ingesting data from third-party sources and sharing data to external partners via APIs.
• Collaborating with analysts and stakeholders to understand data requirements and deliver solutions.
• 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).
• Prior MAANG experience (understands internal infrastructure and tech stack).
• Experience in a fast-paced industry or company (startups acceptable; avoid candidates.

Education/Experience:

• 8+ years of experience doing quantitative and operational analyses in a customer support/service, e-commerce, or order management 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).
• 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.