DATA WAREHOUSE BUSINESS ANALYST

United States Cold Storage, Inc.•Camden, NJ

6d•$100,000 - $130,000•Hybrid

About The Position

Build the intelligence that optimizes the movement of food at national scale. Who We Are US Cold owns and operates one of the most complex temperature-controlled logistics networks in North America. Every day, our systems coordinate the storage and movement of food at national scale across a network of state-of-the-art distribution centers, including multiple highly automated warehouse facilities. We continue to advance our core warehouse and logistics platforms. Our current focus is on modular, event-driven, API-first and cloud architectures. We continue to enhance reliability and accelerate engineering productivity by strengthening our SRE and AI practices. This is a large investment in innovation to continue to drive operational excellence at our facilities. If you want to build durable systems that operate in the physical world at scale, this is that opportunity. The Role We are hiring a Data Warehouse Business Analyst for enabling high-quality, unified, and actionable data within the company’s enterprise data warehouse. This role will focus on gathering business and data requirements, understanding operational data sources (WMS, TMS, ERP, etc.), defining the data model needs, and ensuring accurate integration of cold-chain and logistics data into the data warehouse. You will bridge business users, data architect, data engineers, and analytics teams. You will ensure the data warehouse meets operational, compliance, and strategic reporting needs. This role is ideal for a hands‑on, data‑focused Business Analyst who enjoys working within complex operational systems, translating ambiguous business problems into clear requirements and insights, and seeing those insights drive real decisions in production.

Requirements

5+ years of experience in data/business analysis, preferably with data warehouse or ETL exposure.
Strong understanding of relational databases, data modeling concepts, and SQL.
Experience analyzing data from logistics, supply chain, or operational systems.
Ability to translate business rules into clear data requirements.
Excellent verbal and written communication skills with the ability to interact effectively with stakeholders at all levels.
Strong problem-solving and analytical skills.
Excellent communication and presentation skills.
Ability to work independently and as part of a team.
Strong attention to detail and accuracy.
Bachelor’s degree in business, Information Systems, Data Analytics, Supply Chain, or related field.

Nice To Haves

Experience with cloud data warehouses (Snowflake, BigQuery, Azure Synapse).
Familiarity with ETL/ELT tools (Informatica, ADF).
Experience in cold storage, food logistics, or temperature-controlled industries.
Knowledge of data visualization tools, such as Tableau or Power BI.
Experience with agile development methodologies.
Experience with cloud computing platforms, such as AWS or Azure.
Understanding of database platforms such as Snowflake, Postgres, etc.
Master’s degree preferred

Responsibilities

Translation of business questions into data warehouse requirements by partnering with BI, operations, customer service, finance, logistics, and leadership
Analysis of source systems (WMS, TMS, ERP, customer portals) to identify required data structures, relationships, and integration needs
Definition and stewardship of metrics, data fields, business rules, and transformation logic to ensure shared understanding and consistency
Data mapping and transformation specifications for integrating source data into the data warehouse
Business logic, validation rules, and edge‑case definitions that support data engineers in building and enhancing ETL/ELT pipelines
Participation in data model design (facts, dimensions, star schemas) to ensure models accurately reflect operational and cold‑chain realities
Data validation and reconciliation between source systems and the data warehouse
Identification and resolution of data quality issues, partnering with data engineers and system owners to close gaps and inconsistencies
Documentation of data lineage, definitions, business rules, and data quality standards
Clear, business‑friendly communication of data availability, model changes, and integration timelines
QA test case creation and validation for new data sources, pipelines, and data model enhancements
Impact assessment of upstream system changes (e.g., WMS upgrades, new customer integrations) and coordination of required warehouse updates