Slickdeals

Staff Data Engineer

Slickdeals
US San Jose, CA, US
Onsite 2026-07-02
Announced salary
$195,000 - $260,000
Low
$126K
Median
$166K
High
$217K
Market in San Jose · BLS OEWS 2025
Estimated net pay
$10,949 - $14,156
/month · 33% withheld
after tax & contributions · Single, no dependents

Job description

**About Slickdeals:** We believe shopping should feel like winning. That's why 10 million people come to Slickdeals to swap tips, upvote the best finds, and share the thrill of a great deal. Together, our community has saved more than $10 billion over the past 26 years. We're profitable, passionate, and in the middle of an exciting evolution—transforming from the internet's most trusted deal forum into the go\-to daily shopping destination. If you thrive in a fast\-moving, creative environment where ideas turn into impact fast, you'll fit right in. **The Purpose:** Slickdeals is seeking a Staff Software Engineer with deep expertise in Big Data platforms/systems to lead and evolve our data engineering ecosystem. This role goes beyond pipeline maintenance; it's about architecting scalable, resilient platforms that power analytics, experimentation, and machine learning across the business. You'll inherit a mature platform built over 3\+ years, spanning Databricks, dbt, Airflow, AWS, Tableau, and AtScale, and drive its next phase of growth. As a technical leader, you'll shape architecture, mentor engineers, and ensure our data infrastructure supports analytics, experimentation, and business enablement at scale. **What You'll Do:** * Architect, evolve, and maintain core ETL/ELT pipelines using dbt, Airflow, and Databricks. * Design and optimize semantic models in AtScale to support BI tools like Tableau. * Lead cross\-functional collaboration with Analytics, Product, and Engineering to deliver reliable, timely data. * Own observability, performance, and reliability of data workflows across environments. * Guide infrastructure decisions in AWS (S3, Kafka, EC2, Lambda, IAM), balancing scalability and cost. * Drive cost optimization and platform hygiene across data storage, compute, and tooling. * Champion CI/CD practices and automated testing for data pipelines and infrastructure\-as\-code. * Uphold engineering rigor through SDLC best practices, including ve

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