Data Analytics & Engineering - Data Engineer IV- REMOTE

American IT Systems•Atlanta, GA

1d•Remote

About The Position

We're seeking a data engineer with a strong technical background in data engineering and AI enablement, combined with functional expertise in customer experience insights—specifically in customer support and operations. To excel in this role, you'll need to be passionate about data, adept at managing multiple projects simultaneously, and thrive in a fast-paced environment. You should also possess excellent organizational and presentation skills, be able to build and maintain strong relationships with internal partners and stakeholders, and demonstrate a proven ability to work effectively within cross-functional teams.

Requirements

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

Nice To Haves

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

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