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# Manager, Data Engineering

- **Company:** Publicis Groupe
- **Location:** New York, NY, United States
- **Experience:** Expert
- **Salary:** $88,540.0
- **Contract:** Temporary contract
- **Skills:** Query Performance, Application Programming Interfaces (APIs), Artificial Intelligence, Automation of Tests, Code Review, Data as a Services, Information Engineering, Software Debugging, Distributed Computing Environment, Python (Programming Language), Standard Sql, Software Engineering, AI Coding Agents, Data Layers, Stream Processing, Data Pipelines
- **Published:** October 30, 2026
- **Apply:** https://www.disabledperson.com/jobs/76290224-manager-data-engineering

## About the Role

* 5+ years of data or software engineering experience, including production pipeline delivery and technical leadership.
 * Strong Python and SQL skills, data modeling experience and practical knowledge of API integration and failure handling.
 * Experience with cloud warehouses or lakehouses, orchestration, automated testing and deployment.
 * Regular use of AI development tools, with examples of improving engineering work and catching or correcting generated errors.
 * Ability to diagnose data issues and communicate solutions clearly; a relevant technical degree or equivalent practical experience.
 * How you work with AI
 * Break work into clear tasks, provide useful context to AI tools and review generated code, queries and tests before use.
 * Keep confidential data within approved tools and access boundaries, and judge automation by correctness, maintainability and time saved.
 * Distributed processing experience; streaming systems; semantic layers; data services for AI; or experience in media and advertising technology.

## Description

* Build and operate scalable pipelines that integrate APIs, files and enterprise systems into dependable data products.
 * Design reusable data models and interfaces for analytics, applications and AI use cases.
 * Implement automated quality checks, reconciliation, lineage and recoverable processing so consumers can trust the data.
 * Use AI coding assistants and agents to accelerate pipeline development, testing, debugging and documentation; verify correctness and performance.
 * Develop reusable components and automate repetitive engineering tasks to reduce maintenance and shorten delivery cycles.
 * Monitor reliability, investigate failures and improve query performance, recovery and infrastructure cost.
 * Collaborate with product, AI engineers and data consumers; review code, mentor colleagues and own delivery through production.