Note: The job is a remote job and is open to candidates in USA. Creospan Inc. is a growing tech collective of makers, shakers, and problem solvers, offering solutions today that will propel businesses into a better tomorrow. As a Data Engineer on the Customer Experience team, you will build scalable data pipelines and create insightful dashboards to enhance customer experiences, while also enabling AI-driven analytics and automation capabilities across the team.
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
- 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)
- 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
Company Overview