Digital Syndication Engineer

Newell Brands•Hoboken, NJ

About The Position

This role is a force multiplier for how Newell moves product content to market. As our brands scale across retailers and marketplaces, the quality, speed, and reliability of syndication integrations directly impact digital shelf performance, item setup velocity, and partner trust. The Digital Syndication Integration Engineer is a hands-on technical contributor embedded in our Omni Enablement ecosystem, focused on building and operating the integrations that move product data and assets from enterprise systems into Salsify and onward to retail partners. This role blends integration engineering, automation, and applied problem-solving — with a clear bias toward operational impact.

Requirements

1–3 years of experience in software engineering, systems integration, or platform operations
BA/BS in Computer Science, Software Engineering or similar discipline
Proficiency in Python and JavaScript
Hands-on experience working in Azure cloud environments
Strong experience with REST APIs and integration patterns
Solid understanding of data pipelines, transformations, and error handling

Nice To Haves

Experience with Databricks
Familiarity with SAP, MuleSoft, Adobe Experience Manager, or Stibo Systems
Exposure to PIM, DAM, or retail syndication platforms (e.g., Salsify)
Experience with Power BI or operational reporting
Curiosity and exposure to automation frameworks or applied AI use cases

Responsibilities

Design, build, and support REST-based integrations connecting enterprise platforms (SAP, MuleSoft, Adobe Experience Manager, Stibo Systems) to Salsify and downstream retail channels.
Implement data ingestion, transformation, and synchronization workflows that are reliable, scalable, and observable.
Diagnose and resolve API failures, data quality issues, and performance bottlenecks.
Apply best practices for authentication, error handling, logging, versioning, and documentation.
Develop and operate integration services within Newell’s enterprise Azure environment.
Leverage Databricks for data transformations, validations, and exception handling.
Ensure all solutions align with enterprise security, architecture, and governance standards.
Build automation scripts and lightweight internal tools that reduce manual effort and improve throughput.
Create reusable integration components to accelerate onboarding of new brands, retailers, or data sources.
Experiment with Azure-based AI services or bots to support data quality, monitoring, or operational workflows.
Implement monitoring, alerting, and logging for integration pipelines.
Partner with Analytics to support operational dashboards and Power BI reporting.
Produce clear technical documentation, runbooks, and support procedures.