Large-Scale Across all Levels Layoffs but still 12% Profit, Seriously?!!! - Recensione dipendente - Software Engineer presso Lithia & Driveway

1,0
5 ott 2023
Consiglia
Gradimento del CEO
Pronostico commerciale

Vantaggi

Excellent collaboration among all the engineers. Great people to work with.

Svantaggi

See the headline. It appeared that management decided once they had a basic functioning web app to layoff 1/2 of Driveway thinking they were no longer needed and now they just need to maintain it. Seemed like business people making money-driven decisions thinking a basic web-app is enough without needing a sophisticated search engine, mobile apps, robust navigation, and all the other features provided by e-commerce leaders. Lithia leadership when it comes down to it are still just a bunch of car-dealers without no understanding of tech innovation and keeping the talent needed that can create that innovation. They're all about short-term thinking.

Esplora altre recensioni su Lithia & Driveway

5,0
6 giu 2026
Consiglia
Gradimento del CEO
Pronostico commerciale

Vantaggi

Love what I do. Great team members.

Svantaggi

Very Hot on summer days.

5,0
15 mag 2026
Consiglia
Gradimento del CEO
Pronostico commerciale

Vantaggi

Strong exposure to real business data from automotive sales, finance, inventory, and customer operations. Opportunity to work on high-impact analytics that directly affect dealership performance and revenue. Large-scale company with many datasets and business units, which is good for learning business intelligence and predictive analytics. Growing digital transformation initiatives can provide opportunities to work with modern analytics tools and automation.

Svantaggi

Legacy systems and fragmented data sources may make data cleaning and integration challenging. Traditional corporate structure can sometimes slow down decision-making or the implementation of data-driven ideas. Work may lean more toward reporting/dashboarding than advanced machine learning, depending on the team. Stakeholders may prioritize quick operational insights over long-term data science experimentation. A high-pressure retail environment can lead to tight deadlines and rapidly changing priorities.

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