Data Engineering at 84.51°

The employer trade at 84.51° is specialized depth in retail data science infrastructure in exchange for a ceiling that reflects the company's size and ownership structure. Kroger's subsidiary relationship means resources and strategic direction flow from a parent retailer, not from an independent tech org, and that context shapes everything from tooling decisions to promotion timelines. The Glassdoor rating of 2.7 is toward the bottom of the pack, which is a real signal worth taking seriously. The current neutral internal read with a roughly flat trajectory suggests the floor has stabilized, but engineers who want a high-energy, fast-moving culture will find the pace measured. Pay at L3 lands in line with other Retail companies, so compensation is fair rather than a differentiator. The genuine draw is domain focus: if retail and CPG data is the area you want to build expertise in, the depth of Kroger's data assets is real.

84.51° is the data science and analytics subsidiary that powers Kroger's loyalty and personalization engine, which means the core data problem is retail behavioral data at grocery scale: tens of millions of households, transaction streams from thousands of stores, and a merchandising machine that needs segment-level signals fast enough to influence promotions before they print. Data engineers here are building and maintaining the pipelines that feed recommendation models, coupon targeting, and supplier analytics products sold back to CPG brands. The stack clusters around Databricks, Azure and Kubernetes, which points to a cloud-native lakehouse pattern with containerized workloads. Day-to-day work skews toward batch ETL and feature pipelines for ML consumption, with SQL and PySpark as the primary transformation layer, and enough Databricks surface area that fluency there is a practical requirement from the first week.