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 a nd 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.

Requirements

, not just implement them as given. 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.

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 pipelines using technologies such as Spark and Delta Lake, and building ingestion, transformation, and curation workflows for both structured and unstructured data. You will implement modern data architectures including lakehouse patterns and medallion layering (bronze, silver, gold), ensuring systems are reusable, scalable, and aligned with enterprise needs.

Enable AI, GenAI Data Products Deliver high-quality datasets that support analytics, machine learning, causal modeling, and optimization systems. You will enable data pipelines for GenAI use cases (including LLMs, RAG pipelines, and vector-based data flows), as well as agent-based architectures and intelligent workflows, ensuring that data is model-ready and production-grade. Data Modeling, Orchestration Automation Design scalable logical and physical data models for analytical and operational use cases, ensuring consistency across domains.

Orchestrate workflows using tools such as Airflow, dbt, Lakeflow, or equivalents, with strong focus on automation, reliability, and maintainability of end-to-end pipelines. Architecture, Governance Observability Apply modern architecture patterns including event-driven and streaming architectures, and ensure adherence to best practices in data governance, lineage, quality, and access control (RBAC/ABAC). Establish strong data observability, including monitoring of data freshness, pipeline reliability, and SLA adherence, ensuring systems remain trustworthy and production-ready.

Data Serving, Integration Optimization Enable data serving layers (APIs, feature inputs, analytical endpoints) to support downstream systems, including ML and AI platforms.