Sr. Data Engineer

Job Description

About the Role 

We are seeking a Senior Data Engineer with strong hands-on experience in building scalable data pipelines, microservices, and modern cloud-native data solutions. Beyond traditional data engineering, this role requires someone with high learning agility, a willingness to adopt new platforms, and strong curiosity for enterprise systems that support data operations. 

This individual will serve as a key onsite engineering partner for Product, Business, and cross-functional teams. The engineer will build data workflows, integrate with enterprise platforms, and support end-to-end data lifecycle needs across the organization. 

This position is 4 days in office, 1 day remote per week, based at our corporate headquarters in Raleigh, North Carolina (North Hills) 

Key Responsibilities:

Data Engineering & Architecture 

• Design and build scalable batch and streaming pipelines for ingestion, transformation, and consumption. 

• Develop and optimize ETL/ELT workflows using modern orchestration or transformation tools. 

• Build microservices and data services using Python or Java Spring Boot, leveraging event-driven architecture such as Kafka. 

• Apply strong SQL skills to develop analytical datasets, transformations, and modeling patterns. 

• Build reusable, modular engineering components that support long-term maintainability. 

Cloud & Platform Engineering 

• Design and build cloud-native solutions using any major cloud platform (AWS, Azure, GCP) and be comfortable adopting new cloud services as organizational needs evolve. 

• Work with cloud data warehouses (e.g., Snowflake, BigQuery, Redshift, Synapse) for modeling, performance tuning, and data operations. 

• Implement scalable, secure, and observable cloud data workflows using a cloud-neutral architectural mindset. 

• Develop and maintain CI/CD pipelines across cloud environments using Git-based workflows. 

Enterprise Platform Integration  

• Learn and support enterprise data systems and integration platforms used across the organization. 

• Serve as the primary onsite engineering resource, partnering closely with Product, Business, and Data teams. 

• Support integration, workflow implementations, troubleshooting, and platform enhancements across multiple systems. 

Observability, Quality & Reliability 

• Implement strong observability across pipelines and services, including logging, metrics, dashboards, tracing, and alerting. 

• Build robust data quality checks, validation rules, error handling, and resiliency patterns. 

• Ensure data pipelines and microservices meet reliability, recoverability, and performance standards. 

Collaboration & Delivery 

• Work directly with product and business teams to gather requirements, build prototypes, and deliver production-grade solutions. 

• Provide accurate LOEs and contribute to architectural decisions. 

• Communicate effectively with global and cross-functional teams. 

• Drive engineering standards, documentation quality, and reusable frameworks across the team. 

Innovation & Emerging Technol