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# data engineer

- **Company:** Zedge, Inc.
- **Location:** United States
- **Experience:** Expert
- **Salary:** $60,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Airflow, Big Data, BigQuery, Information Engineering, Data Infrastructure, Data Structures, Cloud Services, Information Technology, Deployment Automation, Terraform, Data Pipelines
- **Published:** June 18, 2026
- **Apply:** https://careers.zedge.net/p/ad45c3cb51bd01-senior-data-engineer

## About the Role

Advanced Degree in computer science, mathematics, data science or related fields.
Proven work experience, at least 5 years of working as a data engineer.
Strong sense of ownership, a can-do attitude and a collaborative, results-driven approach.
Proficiency with Airflow and dbt or similar products (Prefect, Dagster, etc.)
Advanced knowledge of data structures and data modeling.
Hands-on experience with CI/CD pipelines and automated deployment.
Experience with very large data sets (several terabytes per day).
Experience with cloud data platforms is essential, experience with GCP / BigQuery is advantageous.
Familiarity with Terraform or infrastructure-as-code is a plus.
Good communication and presentation skills.

## Description

At Zedge, we rely on data to make decisions and solve problems. We're looking for a highly experienced senior data engineer with strong skills in building and maintaining large data infrastructure.

You will help maintain the systems that empower our data scientists, analysts and senior stakeholders. You will build new, and improve existing, data pipelines and tables so that they are stable, accurate and well monitored. You will contribute to making our data infrastructure robust, stable and well-maintained, and collaborate with other engineers on the best methods, tooling and integration.

Responsibilities

️ Build and maintain pipeline architectures in Airflow and dbt.
️ Assemble large and complex datasets for business requirements.
️ Design systems and make technical decisions with scale, reliability, and quality in mind.
️ Leverage AI tools and agentic workflows to automate routine data engineering tasks.
️ Continuously improve methods, queries and techniques to suit our very large data environment.
️ Uphold best-practice coding and review standards.
️ Notice, investigate, and respond to requests regarding anomalies in the data.
️ Participate in a bi-weekly on-call rotation to monitor pipeline health and respond to data incidents.


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