IT Data Engineer – Palantir: Sales & Trade

ConAgra Foods•Chicago, IL

1d•Hybrid

About The Position

Contribute to the design and implementation of Sales and Trade data architectures, supporting high impact commercial capabilities such as Trade Optimization, Post Event Analytics, Price Pack Architecture, Strategic Pricing, Assortment, Daily Sales Reporting, and Trade Promotion Analytics. Build, enhance, and maintain SAP and Palantir data models, including S4/HANA, BW/HANA, SAP Trade Promotion Management, and Trade Management Application, ensuring data is structured, reliable, and accessible for analytics and reporting. Develop and support data pipelines and integrations across SAP, Palantir, Databricks, Informatica IICS, Mulesoft, Power BI, APIs, and other enterprise systems; familiarity with Circana data models is a plus. Partner closely with Data Architects and Senior Data Engineers to translate architectural designs into working solutions by contributing code, data transformations, and configuration changes across platforms. Implement and follow data quality, validation, and monitoring practices to help ensure data accuracy, integrity, and reliability. Collaborate with business stakeholders, analysts, data scientists, and IT teammates to understand requirements, troubleshoot issues, and deliver incremental improvements to data solutions. Contribute to and/or own the performance and reliability of data workflows by owning issue resolution, root cause analysis, and ongoing optimization. Contribute to and/or own solution discussions and design reviews, providing technical input while learning and applying enterprise standards, non functional requirements, and operational best practices.

Requirements

Bachelor’s degree in Computer Science, Engineering, Data Architecture, or a related field, or equivalent practical experience.
Five to seven years of experience supporting data engineering, analytics, or systems solutions, preferably within Sales, Trade, or commercial domains using platforms such as SAP S4/HANA, BW/HANA, Databricks, Palantir, or similar technologies.
Strong customer facing experience translating technical concepts into business language and vice versa.
Strong hands on experience with SQL and working knowledge of Python or PySpark for data transformation and analysis.
Experience building or supporting data pipelines and integrations using tools such as Databricks, Informatica IICS, Mulesoft, Power BI, and APIs.
Experience of data modeling, ETL processes, and analytics enablement, with the ability to follow and apply established architectural patterns.
Ability to operate effectively in a collaborative, matrixed environment, taking ownership of assigned work while seeking guidance and feedback as needed.

Nice To Haves

Preferred experience in Consumer Packaged Goods and familiarity with Circana data.
Preferred exposure to agile delivery methods and tools such as JIRA or Confluence.

Responsibilities

Contribute to the design and implementation of Sales and Trade data architectures, supporting high impact commercial capabilities such as Trade Optimization, Post Event Analytics, Price Pack Architecture, Strategic Pricing, Assortment, Daily Sales Reporting, and Trade Promotion Analytics.
Build, enhance, and maintain SAP and Palantir data models, including S4/HANA, BW/HANA, SAP Trade Promotion Management, and Trade Management Application, ensuring data is structured, reliable, and accessible for analytics and reporting.
Develop and support data pipelines and integrations across SAP, Palantir, Databricks, Informatica IICS, Mulesoft, Power BI, APIs, and other enterprise systems; familiarity with Circana data models is a plus.
Partner closely with Data Architects and Senior Data Engineers to translate architectural designs into working solutions by contributing code, data transformations, and configuration changes across platforms.
Implement and follow data quality, validation, and monitoring practices to help ensure data accuracy, integrity, and reliability.
Collaborate with business stakeholders, analysts, data scientists, and IT teammates to understand requirements, troubleshoot issues, and deliver incremental improvements to data solutions.
Contribute to and/or own the performance and reliability of data workflows by owning issue resolution, root cause analysis, and ongoing optimization.
Contribute to and/or own solution discussions and design reviews, providing technical input while learning and applying enterprise standards, non functional requirements, and operational best practices.