Data Engineer - Operational Support (Co-op)

Campbell's•Camden, NJ

13d

About The Position

We are seeking a motivated and curious Data Engineer to join our Enterprise Data & Analytics team. This co-op provides hands-on experience building and supporting cloud-based data platforms and pipelines that power enterprise analytics, reporting, and AI/ML solutions. You will work with tools such as Databricks, Snowflake, Azure, ADLS, ADF, Power BI, and SAP integrations in an Agile environment. If you are passionate about data, cloud technologies, and solving complex problems, we want to hear from you.

Requirements

Pursuing a degree in Computer Science, Data Engineering, Data Science, or related fields.
Basic knowledge of SQL and relational databases.
Programming in Python and/or PySpark.
Understanding of ETL/ELT, data modeling, or STTM concepts.
Strong problem-solving and analytical skills.
Strong communication and teamwork skills.
Self-motivated with a willingness to learn.

Nice To Haves

Familiarity with Databricks, Snowflake, Azure, ADF, ADLS, or Power BI is a plus.
Exposure to AI/ML, CI/CD, or DevOps is beneficial.

Responsibilities

Build Data Pipelines: Develop and support scalable data pipelines and ETL/ELT workflows.
Data Ingestion & Integration: Ingest data from SAP, APIs, databases, flat files, and cloud platforms.
Data Transformation: Build transformations using Python, PySpark, and SQL.
Data Modeling & STTM: Support STTM, semantic layers, and data modeling for analytics.
Support Analytics & AI/ML: Prepare datasets for reporting, AI/ML, and advanced analytics.
Monitor & Troubleshoot: Track pipeline health and resolve failures, performance, and data quality issues.
Data Operations: Support incident triage, root cause analysis, and operational monitoring.
BI Collaboration: Support Power BI datasets, semantic models, and reporting solutions.
DevOps & CI/CD: Assist with deployments, testing, and automation.
Agile Delivery: Participate in sprint planning, stand-ups, and retrospectives.
Documentation: Create runbooks, designs, and technical documentation.
Automation & AI: Explore automation and AI-driven improvements.
Monitoring & Troubleshooting: Learn how to monitor platform health, data refreshes, workloads, and integrations; help investigate and troubleshoot failures or performance issues.
Incident & Problem Resolution: Support issue resolution by partnering with senior engineers to perform basic root-cause analysis and document fixes and learnings.

Benefits

Hands-on experience with Databricks, Snowflake, Azure, ADLS, and ADF.
Exposure to ETL/ELT, data modeling, orchestration, and CI/CD.
Mentorship from Data, Platform, and AI/ML Engineers.
Experience working in Agile/Scrum and DevOps environments.
Exposure to enterprise use cases across supply chain, finance, reporting, and AI/ML.

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