6 days ago
Puteaux, FranceSenior
Responsibilities
- Design and implement modern data architectures, including data lakes, lakehouses, data warehouses, data mesh, and data products.
- Build robust batch, micro-batch, and streaming data pipelines from ingestion through BI, APIs, ML features, and RAG exposure.
- Industrialize ML, GenAI, and BI use cases by packaging models, automating workflows, and implementing MLOps and LLMOps practices.
- Design and operate GenAI components including RAG pipelines, vector databases, LLM observability tools, prompt management, and secure model calls.
- Define architecture patterns, tooling, and development standards with client Data Platform Engineering, DevOps, architecture, and security teams.
- Act as a Data Engineering Tech Lead by framing technical work, making architecture decisions, reviewing code, and supporting developers.
- Implement data quality, monitoring, schema management, data contracts, data observability, catalogs, lineage, access security, and compliance practices.
- Design data and technical architectures for agentic workflows and integrate agents with business systems, APIs, and business tools.
- Lead consulting missions, ensure deliverable quality, coordinate stakeholders, and meet client commitments.
- Mentor consultants, develop the Data & AI offering, create internal tools and frameworks, and contribute to proposals and presales.
Requirements
- At least 3 years of experience in Data Engineering and successful experience leading Data Engineer teams.
- Strong knowledge of modern data architectures, including data lakes, data warehouses, lakehouses, medallion architecture, data products, and data mesh patterns.
- Strong command of advanced SQL, Python, and PySpark.
- Significant experience with at least one public cloud: Azure, AWS, or GCP.
- Experience with one or more modern data ecosystem components such as Databricks, Snowflake, Dataiku, Palantir Foundry, or Fabric.
- Knowledge of infrastructure-as-code tools such as Terraform, CloudFormation, or Bicep.
- Experience with containerization using Docker and, optionally, Kubernetes.
- Good knowledge of MLOps practices and initial hands-on experience with LLMOps.
- Excellent interpersonal skills and the ability to lead technical workshops with IT, Data Office, business, and security stakeholders.
- Fluent French and English for international client, partner, and internal-community contexts.
Benefits
- Individual career development through a tailored career model and Wavestone Horizon progression framework.
- Access to extensive training and certification programs in cloud, DevOps, GenAI, and related areas.
- Flexible and mobile smartworking arrangements.
- Recognition as a Great Place To Work 2025, ranked number one in its category among organizations with more than 2,500 employees in France.
- Recruitment and workplace accommodations, including adapted workstations, additional time, and adapted tools, based on confidential and equitable processes.
Tech Stack
Categories
AI ApplicationsData Engineering
