Performics is the Performance Centre of Excellence within Publicis Groupe, delivering AI-powered, data-driven marketing solutions for global brands.
Our flagship platform, OneSuite, 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 OneSuite’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 OneSuite.
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
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Lead the hands-on design, coding, and evolution of DFP’s cloud-native data architecture (AWS/GCP/Snowflake).
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Architect and implement scalable ingestion and transformation pipelines using Fivetran, Databricks, and Airflow/DBT.
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Design and code modular data services and APIs that unify campaign, conversion, and audience datasets across 20+ marketing platforms.
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Build and optimize schemas, transformations, and ETL logic for high-performance, reusable data workflows.
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Contribute directly to the DFP codebase (Python, SQL, Spark) — developing pipelines and infrastructure alongside the engineering team.
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Establish data versioning, testing, and governance frameworks ensuring reliability, lineage, and compliance.
Data Engineering Leadership
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Drive best practices in data modeling, CI/CD, code reviews, and performance optimization.
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Collaborate across engineering teams to define data contracts, schema validation rules, and automated quality checks.
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Mentor junior engineers by pairing on code, reviewing pull requests, and helping them deepen technical maturity.
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Participate actively in sprint planning and retrospectives, ensuring engineering execution aligns with platform goals.
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Partner with Product and Data Science teams to translate analytical requirements into efficient, production-grade data solutions.
AI-Ready Infrastructure
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Design and maintain pipelines supporting Retrieval-Augmented Generation (RAG) and Context Engine™ workflows.
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Build high-performance APIs and caching strategies enabling low-latency data access for AI agents and orchestration systems.
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Implement vector-based data retrieval layers (pgVector, Pinecone) and ensure efficient embedding pipelines for AI contexts.
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Partner with AI teams to monitor data latency, cost efficiency, and observability metrics.
Collaboration & Governance
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Partner with Platform Operations and Security to enforce privacy, compliance, and access control frameworks (GDPR, SOC2).
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Work cross-functionally with analysts, AI engineers, and platform leads to deliver production-grade, business-critical data products.
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Lead by example in data documentation, pipeline testing, and lineage tracking — ensuring transparency and reproducibility across all pipelines.
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Hands-on experience as a Data Engineer or Data Platform Developer, including direct contributions to production codebases.
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Expert-level proficiency in Python, SQL, and Apache Spark for data pipeline development.
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Proven experience building ELT workflows using Fivetran, Airflow, DBT, or Databricks.
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Solid understanding of data modeling, dimensional design, and schema normalization at enterprise scale.
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Strong experience with AWS (S3, Redshift, Glue, Lambda) or GCP (BigQuery, Dataflow).
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Experience working with marketing or ad platform data (Google Ads, Meta, TikTok, DV360, Amazon Ads).
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Demonstrated expertise in code versioning (GitHub), CI/CD integration, and data observability practices.
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Ability to write clean, modular, testable code and review peers’ contributions for maintainability and performance.
