Sr Analyst, Operations

Since 1869, we've connected people through food they love. We’re proud to be stewards of amazing brands that people trust. Our portfolio includes the iconic Campbell’s brand, as well as Cape Cod, Chunky, Goldfish, Kettle Brand, Lance, Late July, Pacific Foods, Pepperidge Farm, Prego, Pace, Rao’s Homemade, Snack Factory, Snyder’s of Hanover. Swanson, and V8. Here, you will make a difference every day.

You will be supported to build a rewarding career with opportunities to grow, innovate and inspire. Make history with us.

Why Campbell’s… Benefits begin on day one and include medical, dental, short and long-term disability, AD&D, and life insurance (for individual, families, and domestic partners).

Employees are eligible for our matching 401(k) plan and can enroll on the first day of employment with immediate vesting.

Campbell’s offers unlimited sick time along with paid time off and holiday pay. If in WHQ – free access to the fitness center. Access to on-site day care (operated by Bright Horizons) and company store.

Giving back to the communities where our employees work and live is very important to Campbell’s. Our “Campbell’s Cares” program matches employee donations and/or volunteer activity up to $1,500 annually.

Campbell’s has a variety of Employee Resource Groups (ERGs) to support employees.

As a Senior Analyst – Application Operations, you will be a hands‑on member of the Data Analytics and AI Operations team, responsible for operating, monitoring, troubleshooting, and continuously improving enterprise data and analytics platforms. You will bring strong Data Engineering and Data Analytics experience to ensure reliable data pipelines, high-quality data, actionable insights, and trusted analytics. You will leverage industry-leading technologies including Databricks, Snowflake, Azure Data Factory (ADF), Informatica, Python, SQL, Micro Strategy and Power BI to deliver scalable, secure, and high-performing data solutions.

Your work will help drive digital transformation, enable AI innovation, and ensure trusted data is available to stakeholders across the enterprise. You will also apply working knowledge of AI and Machine Learning to support evolving data, analytics, and AI capabilities. The technologies and responsibilities listed below are not limited to those specifically identified.

• Perform hands‑on Data Engineering and Data Analytics across enterprise data platforms and solutions.
• Build, monitor, troubleshoot, and optimize ETL/ELT pipelines, workflows, integrations, and data jobs.
• Perform hands‑on SQL and Python data analysis, validation, reconciliation, profiling, and root cause analysis.
• Support the data pipelines across SAP S/4
  
  HANA, SAP Datasphere, SAP SLT, ERP, APIs, cloud applications, and other enterprise systems.
• Analyze and resolve data quality, pipeline, performance, and integration issues.
• Develop and support Power BI, Micro Strategy, reports, dashboards, KPIs, metrics, and operational analytics.
• Create and maintain trusted data products, curated datasets, and semantic layers that support enterprise analytics and AI use cases.
• Support and maintain curated datasets, semantic layers, data models, and enterprise reporting.
• Support data administration activities, including access, permissions, user management, job scheduling, environment support, monitoring, and data platform operations.
• Develop Python/SQL automation to improve efficiency and reduce manual effort.
• Monitor data and platform performance and proactively identify operational risks.
• Lead incident investigation, troubleshooting, resolution, and corrective actions.
• Maintain runbooks, SOPs, monitoring standards, and technical documentation.
• Drive data quality, governance, security, lineage, metadata, and compliance practices.
• Identify opportunities for automation, optimization, reliability, and continuous improvement.
• Apply knowledge of AI/ML concepts and data requirements to support emerging analytics and AI initiatives.
• Serve as a hands‑on technical resource for data and analytics issues.

• Data Engineering teams supporting enterprise data platforms, pipelines,…