Senior Data Engineer

🕒 il y a 1 mois

🇵🇱 Pologne – Télétravail

⏰ Temps Plein

🟠 Senior

🚰 Ingénieur Data

👻 Score fantôme 27%

Ce que ça veut dire : la probabilité que personne ne recrute vraiment pour cette offre.Comment le lire : 60 % ou plus, on pense que c'est une offre fantôme. Un score plus bas est moins inquiétant.Comment on la calcule : ancienneté, republications, postes ouverts vs taille de l'équipe, rapidité de clôture, et si le salaire est indiqué.À noter : une estimation à partir de données publiques, pas un jugement sur l'entreprise. De vraies offres peuvent avoir un score élevé, alors prenez-le comme un indice, pas comme un fait.

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Valtech

Site WebLinkedInTous les Emplois

5001 - 10000 employés

Fondée en 1997

💼 Conseil

📣 Marketing

☁️ SaaS

Consulting • Marketing • SaaS

Valtech est un cabinet de conseil mondial en expérience digitale et en transformation, qui se présente comme « The Experience Innovation Company ». Nous aidons les entreprises et grands comptes à concevoir et à construire des expériences client mémorables, à accélérer le commerce omnicanal, à piloter leur transformation digitale et à renforcer la créativité et la performance marketing. Valtech propose également des services de croissance pilotés par la data et l’AI et intervient dans de nombreux secteurs, du B2B au retail et à la mobilité, avec une présence mondiale, des bureaux locaux et des partenariats clients.

Description

• Design, build, and optimize modern cloud-based data platforms powering analytics, AI, and data products • Develop scalable batch, streaming, and near-real-time pipelines • Deliver high-quality curated datasets with governance, security, and observability • Support AI and GenAI systems, including machine learning, causal modeling, RAG, and agent-based applications • Design and implement platforms and pipelines primarily on AWS and Databricks, and on GCP or Azure/Fabric as required • Build ingestion, transformation, and curation workflows for structured and unstructured data • Implement lakehouse patterns and medallion layering • Contribute to cloud-to-cloud migrations, including re-platforming pipelines and data models • Deliver production-grade datasets for analytics, ML, causal modeling, and optimization • Build GenAI pipelines involving LLMs, RAG, vector-based data flows, and agent-based architectures • Leverage Databricks MLflow and Unity Catalog or equivalent tools • Design logical and physical data models • Orchestrate workflows using Databricks Workflows, Airflow, dbt, or equivalents • Apply event-driven and streaming architecture patterns • Ensure data governance, lineage, quality, and access control using Unity Catalog, AWS Lake Formation, Microsoft Purview, or GCP Dataplex • Establish observability for data freshness, pipeline reliability, and SLA adherence • Enable serving layers such as APIs, feature inputs, and analytical endpoints for ML and AI platforms • Optimize pipelines and infrastructure for performance, scalability, and cost • Discover requirements by understanding stakeholder needs and business objectives • Identify gaps and propose better alternatives • Collaborate with data scientists, ML engineers, analysts, platform teams, and business stakeholders • Support adoption of data products and contribute to best practices

🎯 Exigences

• Strong hands-on experience with Apache Spark and Delta Lake • Strong Python and SQL skills • Proven experience building batch and streaming pipelines and production-grade data platforms • Solid understanding of data modeling, data quality, and governance • Strong, demonstrable hands-on experience with the Databricks Lakehouse Platform, ideally on AWS • Deep expertise in at least one major cloud: AWS, GCP, or Azure/Fabric • Demonstrable openness and ability to work across clouds • Experience with lakehouse architectures and distributed systems • Strong understanding of scalability, reliability, and performance • Curiosity, strong problem-solving skills, and a collaborative approach in cross-functional teams • Comfortable challenging business requirements • Adaptable and eager to work across platforms • Hands-on experience across more than one cloud or cloud migration experience (nice to have) • Snowflake experience (nice to have) • GenAI and AI data systems experience (nice to have) • CI/CD for data pipelines and infrastructure-as-code, including Terraform and CloudFormation (nice to have) • Streaming technologies such as Kafka, Spark optimization, or ML/experimentation platforms (nice to have) • Experience building data products or large-scale analytics platforms (nice to have)

🏖️ Avantages

• 24 working days of paid vacation • National holidays covered • Sick leave (up to 20/year) • Unpaid leave (up to 20/year) • Medical insurance • Multisport card OR Multikafeteria • Maternity & paternity leave support • Internal workshops & learning initiatives • Professional certifications reimbursement • Participation in professional local & global communities • Growth Framework to manage expectations and define the steps to move towards the selected career • Mentoring program with the ability to become a mentor or a mentee to grow to a higher position • Progressive benefit packages—the longer you stay with the company, the more benefits you get • Remote and hybrid work options (country-dependent) • Career advancement, with international mobility and professional development programs • Access to cutting-edge tools, training and industry experts

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