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Slickdeals Tech Stack

Community-driven deal marketplace with ML-powered recommendations

Software Development San Mateo, CA 51–200 employees Founded 1999 Privately Held
slickdeals.net LinkedIn

Slickdeals operates a deal-aggregation and shopping community platform built on a modern data stack (Databricks, dbt, Airflow, Kafka, SageMaker). Active projects reveal a company in transition: core work spans ETL pipeline maturity, ML recommendation systems, and Android modernization (Jetpack Compose adoption), while pain points cluster around scaling recommendation latency and transforming from forum-centric to commerce-centric product. Hiring velocity is accelerating across engineering and data roles, suggesting investment in infrastructure and ML infrastructure.

Tech Stack 36 technologies

Core StackDatabricks dbt Apache Airflow AWS Tableau Kafka AWS Lambda Python Intune Okta Zoom Slack Jira Confluence Kotlin Java Greenhouse SageMaker PyTorch TensorFlow Elasticsearch AtScale IAM macOS Windows 11 JAMF Zoom Rooms Jira Service Desk Android HBase+5 more
AdoptingJetpack Compose

What Slickdeals Is Building

◆Challenges

  • Transforming from deal forum to shopping destination
  • Scaling product features
  • Performance and scalability improvements
  • Scalable data platform
  • Platform hygiene
  • Evolving business model
  • Reducing training cost
  • Scaling recommendation traffic
  • Improving recommendation latency
  • Improving accounts payable efficiency

▲Active Projects

  • Jetpack compose adoption
  • Modularization
  • Performance and scalability improvements
  • Core etl/elt pipelines
  • Semantic models in atscale
  • Observability and reliability of data workflows
  • Office events
  • Hr initiatives
  • Candidate experience feedback
  • Production ml systems for recommendations

Hiring Activity

Accelerating10 roles · 9 in 30d

Department

Data
2
Engineering
2
HR
2
Design
1
Finance
1
Marketing
1
Product
1

Seniority

Junior
3
Mid
3
Senior
3
Staff
1
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About Slickdeals

Slickdeals is a deal-curation and shopping community where users share, vet, and vote on offers across retailers. Founded in 1999 and based in San Mateo, the company operates a website, mobile app (iOS and Android), and browser extension. The platform generates revenue through advertising alongside an evolving business model. With 51–200 employees, the team is split between core engineering, data, design, product, and support functions, hired exclusively in the United States.

HeadquartersSan Mateo, CA
Company Size51–200 employees
Founded1999
Hiring MarketsUnited States

Frequently Asked Questions

What is Slickdeals' tech stack?

Slickdeals uses Databricks, dbt, Apache Airflow, AWS, Kafka, Elasticsearch, SageMaker, PyTorch, and TensorFlow for core platform and ML. Engineering runs on Java, Kotlin, and Python. Frontend includes Android (Jetpack Compose adoption in progress).

What is Slickdeals working on?

Active projects include ML recommendation systems, ETL pipeline scaling, Jetpack Compose adoption, semantic modeling in AtScale, and data workflow observability. Pain points center on recommendation latency, platform scalability, and evolving the business model.

How this profile is built

Slickdeals's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →

This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.