WHO YOU’LL WORK WITH

As a TEAM we interact with Supply chain and End to End fulfillment teams across the globe. It is our responsibility to partner with program, product and engineering peers to ensure we are aligning appropriate technology outcomes with prioritized product initiatives. Beyond our team, collaboration with other global engineering teams, business stakeholders, and technical peers to ensure solutions delivered by our team is essential.

WHO WE ARE LOOKING FOR

We are seeking an experienced Data engineer to join our team. As a key member of OMS team, you will designs, builds, and maintain scalable data infrastructure and pipelines, ensuring alignment with business requirements and industry best practices.

This role supports data scientists and analysts by ensuring reliable access to clean, structured data for analytics and machine learning.

• Bachelor's degree in Computer Science, Information Technology, or related field

• 5+ years of experience in data engineering or related roles.

• Strong experience with OMS products (Sales Force, Manhattan...)

• Strong proficiency in Python, SQL, and data pipeline frameworks.

• Experience with cloud services (AWS, GCP, Azure).

• Familiarity with containerization tools (Docker, Kubernetes).

• Knowledge of data modeling, stream processing, and message queuing systems.

• Background in computer science, statistics, or related fields

• Certifications in AWS, GCP, or other cloud platforms.

• Experience with BI tools like Tableau or Power BI.

• Understanding of machine learning workflows and deployment.

WHAT YOU’LL WORK ON

• Support OMS Implementation

• Design and implement ETL/ELT processes for data ingestion and transformation.

• Build and maintain data warehouses and data lakes.

• Develop and optimize data pipelines using tools like Spark, Python, SQL, and cloud platforms (AWS, GCP, Azure).

• Collaborate with cross-functional teams to improve data models and support business intelligence.

• Ensure data quality, consistency, and governance across systems.

• Deploy machine learning models into production environments.

• Lead junior engineers and promote best practices in data engineering.

• Support reporting needs for OMS

• Lead data extraction and data migration needs for OMS

• Collaborate across teams (Product, architect, engineering) to support data needs for OMS.