Senior Data Engineer

Company Description

Company Description Publicis Media harnesses the power of modern media through leading agency brands
Dysrupt, Infinite Roar, Publicis Collective, Publicis Health Media, Spark Foundry, Starcom
and
Zenith
, as well as global accelerator
PMX
; immersive experience group PMCI and access to integrated platform-based technologies and offerings from CJ, Epsilon and Influential. A key business solution of
Publicis Groupe
, Publicis Media’s digital-first, data-driven global solutions deliver client value and drive growth in a platform-powered world. It is present in over
100 countries with over 23,000 employees worldwide.

Performics is the Performance Centre of Excellence within Publicis Groupe, delivering AI-powered, data-driven marketing solutions for global brands.

Our flagship platform, One Suite, unifies automation, orchestration, and intelligent data systems to transform how performance marketing operates across Search, Social, Programmatic, and Commerce channels.

At the heart of this ecosystem is the Data Foundation Platform (DFP) — the unified data layer that powers One Suite’s analytics, AI orchestration, and decisioning capabilities.

DFP ingests, transforms, and standardizes data from 20+ marketing platforms, enabling intelligent agents, real-time optimization, and cross-channel performance insights.

We’re seeking a Senior Data Engineer to lead the technical direction and architecture of the Data Foundation Platform within One Suite.

This is a hands‑on engineering role — you’ll contribute directly to production code, build pipelines, and review implementations while shaping the long‑term data platform architecture.

You’ll operate as the technical authority for DFP — guiding design patterns, ensuring code quality, and solving complex problems in distributed data systems. You’ll collaborate closely with peer charters to ensure the data foundation remains reliable, scalable, and production‑ready.

Data Platform Architecture

• Lead the hands‑on design, coding, and evolution of DFP’s cloud‑native data architecture (AWS/GCP/Snowflake).
• Architect and implement scalable ingestion and transformation pipelines using Fivetran, Databricks, and Airflow/DBT.
• Design and code modular data services and APIs that unify campaign, conversion, and audience datasets across 20+ marketing platforms.
• Build and optimize schemas, transformations, and ETL logic for high-performance, reusable data workflows.
• Contribute directly to the DFP codebase (Python, SQL, Spark) — developing pipelines and infrastructure alongside the engineering team.
• Establish data versioning, testing, and governance frameworks ensuring reliability, lineage, and compliance.

Data Engineering Leadership

• Drive best practices in data modeling, CI/CD, code reviews, and performance optimization.
• Collaborate across engineering teams to define data contracts, schema validation rules, and automated quality checks.
• Mentor junior engineers by pairing on code, reviewing pull requests, and helping them deepen technical maturity.
• Participate actively in sprint planning and retrospectives, ensuring engineering execution aligns with platform goals.
• Partner with Product and Data Science teams to translate analytical requirements into efficient, production‑grade data solutions.

AI‑Ready Infrastructure

• Design and maintain pipelines supporting Retrieval‑Augmented Generation (RAG) and Context Engine™ workflows.
• Build high-performance APIs and caching strategies enabling low‑latency data access for AI agents and orchestration systems.
• Implement vector-based data retrieval layers (pg Vector, Pinecone) and ensure efficient embedding pipelines for AI contexts.
• Partner with AI teams to monitor data latency, cost efficiency, and observability metrics.

Collaboration & Governance

• Partner with Platform Operations and Security to enforce privacy, compliance, and access control frameworks (GDPR, SOC2).
• Work cross‑functionally with analysts, AI engineers, and platform leads to deliver production‑grade, business‑critical data products.
• Lead by example in data documentation, pipeline testing, and lineage tracking — ensuring transparency and reproducibility…