Senior Data Operations & Analytics Engineer

Campbell Soup Company

Camden (NJ)

On-site

USD 101,000 - 139,000

Full time

14 days+

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Benefits offered by this job

Health insurance

401(k) plan

Paid time off

Job summary

Campbell Soup Company is seeking a Senior Analyst – Application Operations to actively operate, monitor, and improve enterprise data and analytics platforms. You’ll ensure reliable data pipelines, high-quality data, and trusted analytics using Databricks, Snowflake, ADF, Informatica, Python, SQL, MicroStrategy and Power BI to enable AI and digital transformation across the business.

You will collaborate with Data Engineering, Analytics, Platform Engineering, Data Science, and business

Qualifications

Bachelor’s degree in a technical field.
5+ years in both Data Engineering and Data Analytics.
Strong SQL for analysis, debugging, and validation.
Experience building and optimizing ETL/ELT with Databricks and Snowflake.
Hands-on Databricks and/or Snowflake experience.
Experience with ADF, Informatica, ADLS or similar cloud data tools.
Python for data analysis, automation, and ops.
Power BI and MicroStrategy for analytics and reporting.
Knowledge of data modeling, quality, governance, lineage and security.
Production support, monitoring and incident resolution.

Responsibilities

Perform hands-on Data Engineering and Data Analytics across enterprise data platforms.
Build, monitor, and optimize ETL/ELT pipelines and data jobs.
Analyze data with SQL and Python; validate and reconcile datasets.
Support data pipelines across SAP S/4HANA, SAP Datasphere, ERP, APIs, cloud apps.
Identify and resolve data quality, performance, and integration issues.
Develop and support Power BI and MicroStrategy reports and dashboards.
Create trusted data products and semantic models for analytics and AI use cases.
Maintain datasets, data models, and enterprise reporting.
Handle data administration: access, permissions, job scheduling, environments.
Automate with Python/SQL to improve efficiency.
Monitor platform performance and address operational risks.
Lead incident investigations and corrective actions.
Maintain runbooks and technical documentation.
Advance data governance, security, and metadata practices.
Identify automation and reliability improvements; support AI initiatives.
Serve as hands-on resource for data and analytics issues.