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Publicis Media

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Senior Data Engineer

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Summary

A hands-on leadership role leading the Data Foundation Platform within OneSuite at Publicis Media: designing cloud-native data pipelines and models (AWS/GCP, Snowflake, Python, SQL, Spark) to unify 20+ marketing platforms, building RAG/AI-agent pipelines, and mentoring engineers while enforcing governance like GDPR and SOC2.

Overview

In this role you will lead the technical direction and architecture of the Data Foundation Platform within OneSuite, delivering scalable data solutions for multi-channel marketing. You will build and optimize cloud-native pipelines, collaborate with cross-functional teams, and ensure production readiness and governance. You will influence design patterns and drive reliable, observable data systems at scale. This is a hands-on leadership role with a clear impact on performance marketing capabilities.

Pay / Benefits
  • WORK YOUR WORLD
  • REFLECTION DAYS
  • HELP@HAND benefits
  • FAMILY FRIENDLY POLICIES
  • FLEXIBLE WORKING
  • GREAT LOCAL DISCOUNTS
Responsibilities
  • Design, code, and evolve DFP's cloud-native data architecture (AWS/GCP/Snowflake) and scalable ingestion pipelines.
  • Implement modular data services and APIs to unify datasets across 20+ marketing platforms.
  • Develop high-performance ETL/ELT workflows and data models; contribute to the DFP codebase (Python, SQL, Spark).
  • Establish versioning, testing, and governance for reliability and compliance.
  • Drive data modeling, CI/CD, code reviews, and performance optimization across teams.
  • Mentor junior engineers; participate in sprint planning and ensure alignment with platform goals.
  • Design RAG and Context Engine pipelines; build low-latency APIs and caching for AI agents.
  • Ensure privacy, compliance, and access controls (GDPR, SOC2); promote data documentation and lineage.
Key requirements
  • Hands-on Data Engineer experience with production code contributions.
  • Expert Python, SQL, and Spark for data pipelines.
  • Proven ELT workflow experience with Fivetran, Airflow, DBT, or Databricks.
  • Enterprise-scale data modeling, dimensional design, and schema normalization.
  • Experience with AWS (S3, Redshift, Glue, Lambda) or GCP (BigQuery, Dataflow).
  • Experience with marketing/ad platform data (Google Ads, Meta, TikTok, DV360, Amazon Ads).
  • Git/GitHub, CI/CD, and data observability practices.
  • Ability to write clean, modular, testable code and review peers’ contributions.
  • collaboration
  • mentoring
  • organization
  • Python
  • SQL
  • Apache Spark

Skills

What Senior Data Engineering jobs ask for — and how much of it you have →
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See also

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