Valtech is hiring a Senior Data Engineer in Sofia, Bulgaria, to design, build, and optimize modern, cloud-based data platforms that power analytics, AI, and data products. You will work on scalable batch, streaming, and near-real-time data pipelines, ensuring high-quality, curated datasets with robust data governance, security, and observability across the data ecosystem. The role supports AI and GenAI systems, enabling pipelines for machine learning, causal modeling, and LLM-powered applications such as RAG and agent-based systems. You can be deployed on either AWS (with Databricks) or Azure/Fabric (with Databricks or Fabric tooling), and you’ll be matched to projects that align with your strongest platform. Additional experience with Snowflake or GCP is a plus. You will collaborate with data scientists, ML engineers, and platform teams to ensure a production-grade data foundation for AI systems. Responsibilities include building scalable data platforms and pipelines, implementing lakehouse architectures with bronze/silver/gold patterns, and enabling data ingestion, transformation, and curation for structured and unstructured data. You will implement data architectures with governance, lineage, quality, and access control using Unity Catalog, AWS Lake Formation, or Purview, depending on the platform. You will establish data observability and monitor data freshness and pipeline reliability. You will enable data serving layers (APIs, feature inputs, analytical endpoints) for downstream systems, including ML/AI platforms, and continuously optimize pipelines for performance an

Valtech is hiring a Senior Data Engineer in Sofia, Bulgaria, to design, build, and optimize modern, cloud-based data platforms that power analytics, AI, and data products. You will work on scalable batch, streaming, and near-real-time data pipelines, ensuring high-quality, curated datasets with robust data governance, security, and observability across the data ecosystem. The role supports AI and GenAI systems, enabling pipelines for machine learning, causal modeling, and LLM-powered applications such as RAG and agent-based systems. You can be deployed on either AWS (with Databricks) or Azure/Fabric (with Databricks or Fabric tooling), and you’ll be matched to projects that align with your strongest platform. Additional experience with Snowflake or GCP is a plus. You will collaborate with data scientists, ML engineers, and platform teams to ensure a production-grade data foundation for AI systems. Responsibilities include building scalable data platforms and pipelines, implementing lakehouse architectures with bronze/silver/gold patterns, and enabling data ingestion, transformation, and curation for structured and unstructured data. You will implement data architectures with governance, lineage, quality, and access control using Unity Catalog, AWS Lake Formation, or Purview, depending on the platform. You will establish data observability and monitor data freshness and pipeline reliability. You will enable data serving layers (APIs, feature inputs, analytical endpoints) for downstream systems, including ML/AI platforms, and continuously optimize pipelines for performance an