Senior Data Engineer

🕒 vor 1 Monat

🇵🇱 Polen – Remote

⏰ Vollzeit

🟠 Senior

🚰 Dateningenieur

👻 Geisterscore 27%

Was es bedeutet: die Wahrscheinlichkeit, dass für diese Stelle niemand wirklich einstellt.So liest du es: 60 % oder höher heißt, wir halten das für einen Geisterjob. Niedrigere Werte sind weniger bedenklich.Wie wir werten: Dauer offen, Neuveröffentlichungen, offene Stellen vs Teamgröße, wie schnell Stellen geschlossen werden, und ob ein Gehalt angegeben ist.Hinweis: eine Schätzung aus öffentlichen Daten, kein Urteil über das Unternehmen. Echte Stellen können hoch punkten, nutze es also als Hinweis, nicht als Fakt.

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Valtech

WebsiteLinkedInAlle Stellen

5001 - 10000 Mitarbeiter

Gegründet 1997

💼 Beratung

📣 Marketing

☁️ SaaS

Consulting • Marketing • SaaS

Valtech ist eine globale Beratung für digitale Erlebnisse und Transformation, die sich als "The Experience Innovation Company" positioniert. Das Unternehmen unterstützt Enterprise-Kunden dabei, einprägsame Kundenerlebnisse zu konzipieren und umzusetzen, Omnichannel-Commerce zu beschleunigen, digitale Transformation voranzutreiben und Marketing-Kreativität sowie -Performance zu steigern. Darüber hinaus bietet Valtech datengestützte und KI-getriebene Growth-Services und arbeitet branchenübergreifend – von Enterprise über Retail bis Mobilität – und ist weltweit aktiv mit lokalen Büros und Kundenpartnerschaften.

Beschreibung

• 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

🎯 Anforderungen

• 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)

🏖️ Vorteile

• 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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