Valtech
SeniorDataEngineer
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optimal for Senior candidates.
“Senior Data Engineer at Valtech. Skills: Data Engineering, Cloud Platforms, Data Architecture, AI/GenAI Pipelines. Design data platforms. Build data platforms”
Industry & Context.
Problem-solving skills
What They're Looking For.
Must Have
5+ years Python, 5+ years SQL, Experience with data modeling, Experience with data quality, Experience with data governance
Nice to Have
Azure experience transferable to Fabric, Experience with GenAI, Experience with AI data systems, Experience with RAG pipelines, Experience with vector databases, Experience with LLM data preparation, CI/CD for data pipelines, Infrastructure-as-code tools, Experience with Kafka, Spark optimization experience, Advanced analytics experience, ML workloads experience, Causal modeling experience, Experimentation platforms experience, Experience building data products, Experience building large-scale analytics platforms
What You'll Do.
Design data platforms
Optimize data platforms
Build scalable pipelines
Build streaming pipelines
Build near-real-time pipelines
Ensure data governance
Ensure data observability
Support GenAI systems
Enable causal modeling
Enable LLM applications
Enable agent-based systems
Implement data platforms
Build ingestion workflows
Build transformation workflows
Build curation workflows
Implement lakehouse patterns
Implement medallion layering
Deliver high-quality datasets
Enable data pipelines for GenAI
Ensure data is model-ready
Ensure data is production-grade
Orchestrate workflows
Ensure pipeline reliability
Ensure pipeline maintainability
Apply architecture patterns
Ensure adherence to best practices
Establish data observability
Monitor data freshness
Monitor pipeline reliability
Monitor SLA adherence
Enable data serving layers
Support downstream systems
Monitor pipeline performance
Optimize pipeline scalability
Optimize pipeline cost efficiency
Translate requirements into solutions
Support adoption of data products
Contribute to best practices
How You'll Work.
Team & Collaboration
Cross-functional teams; Data scientists; ML engineers; Platform teams; Business stakeholders; Analysts
Process & Methodology
Agile
Full Job Description
Why Valtech? We’re the experience innovation company - a trusted partner to the world’s most recognized brands. To our people we offer growth opportunities, a values-driven culture, international careers and the chance to shape the future of experience. The opportunity At Valtech, you’ll find an environment designed for continuous learning, meaningful impact, and professional growth. Whether you're pioneering new digital solutions, challenging conventional thinking or building the next generation of customer experiences, your work will help transform industries. We are proud of: The work we do and the innovation we drive Our values of share, care and dare A workplace culture that fosters creativity, diversity and autonomy Our borderless, global framework, which enables seamless collaboration The role We are looking for an experienced Senior Data Engineer to design, build, and optimize modern, cloud-based data platforms that power analytics, AI, and data products across the organization. You will work on scalable batch, streaming, and near-real-time pipelines, enabling high-quality, curated datasets while ensuring robust data governance, security, and observability across the data ecosystem. You will also play a key role in supporting AI and GenAI systems, enabling pipelines for machine learning, causal modeling, and LLM-powered applications such as RAG and agent-based systems. Our preferred platforms are Microsoft Azure / Fabric (primary), GCP, AWS, Databricks, and Snowflake, with Azure experience being highly transferable to Fabric. You will collaborate closely with data scientists, ML engineers, and platform teams to ensure the data foundation supports production-grade, decision-oriented AI systems. Role responsibilities Build & Data Platform Engineering Design and implement scalable data platforms and pipelines across cloud environments (Azure/Fabric, AWS, GCP, Databricks, Snowflake). This includes developing reliable batch, streaming, and near-real-time pipeline
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