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# Senior Data and Platform Engineer

- **Company:** The Ladders
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Salary:** $140,000.0 - $270,250.0
- **Contract:** Permanent contract
- **Skills:** Databases, Data Infrastructure, Extract Transform Load (ETL), Software Debugging, Distributed Computing Environment, Distributed Systems, Python (Programming Language), Operational Data Store, SQL Databases, Backend, Information Technology, Data Management, Data Delivery, Stream Processing, Data Pipelines
- **Published:** October 9, 2026
- **Apply:** https://arc.dev/remote-jobs/j/redirect/pq02iv2yap

## About the Role

* BS or MS in Computer Science, Engineering, or a related field, or equivalent experience
 * 5+ years of experience building and operating production software, data platforms, backend infrastructure, databases, or distributed systems
 * Strong proficiency in Python and SQL, with a focus on backend or systems languages
 * Hands-on experience with distributed data processing, relational or analytical database architecture, and production ETL or streaming systems
 * Proven debugging skills across complex systems and multiple layers
 * Experience in a cloud production environment, managing services or pipelines
 * Working knowledge of secure platform development practices

## Description

This role will focus on building and operating the data and platform foundations that power telemetry, operational insights, and dependable internal workflows. The engineer will own critical systems end to end, partnering with teams across the organization to improve reliability, performance, security, and the overall quality of data consumption experiences. This is an opportunity to contribute to a technically advanced environment where scalable infrastructure and trustworthy data delivery are essential to business execution., * Own systems end to end, from assessment through ongoing support

 * Build reliable data pipelines for telemetry and operational data
 * Develop shared libraries and workflows that strengthen the data platform
 * Engineer dependable distributed workloads and troubleshoot performance issues
 * Implement security measures throughout the system lifecycle
 * Establish automated quality and operational checks
 * Deliver user-friendly and trustworthy data consumption experiences