Senior Data Engineer

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.   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 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. Continuously monitor and optimize pipelines and infrastructure for  performance, scalability, and cost efficiency .   Collaboration   Work closely with data scientists, ML engineers, analysts, and business stakeholders to translate requirements into robust data solutions. Support adoption of data products and contribute to best practices across the data and AI ecosystem.   Must have qualifications Technical skills   Strong hands-on experience with  Apache Spark and Delta Lake , and strong programming skills in  Python and SQL . Proven experience building  batch and streaming data pipelines  and production-grade data platforms, with solid understanding of  data modeling, data quality, and governance principles .   Cloud & Platforms (Key Requirement)   Experience with one or more major cloud platforms, with preference for  Microsoft Azure / Fabric , as well as AWS or GCP. Familiarity with modern data platforms such as  Databricks and Snowflake  is expected.   Architecture & Systems Thinking   Experience with  lakehouse architectures and distributed data systems , and strong understanding of scalability, reliability, and performance considerations in data pipelines.   Mindset   Strong problem-solving skills focused on scalability and reliability, with a collaborative approach to working in cross-functional teams. Experience in  Agile or consulting environments  is beneficial.   Nice to have qualifications    Experience with  GenAI and AI data systems  (e.g., RAG pipelines, vector databases, LLM data preparation), as well as CI/CD for data pipelines and infrastructure-as-code tools such as Terraform, ARM, or CloudFormation.   Additional exposure to  streaming technologies (e.g., Kafka) , Spark optimization, or advanced analytics and ML workloads (including causal or experimentation platforms) is valuable. Experience building  data products or large-scale analytics platforms  is also beneficial.   Commitment to reaching all kinds of people   We design experiences that work for all kinds of people - and that starts with our own teams. At Valtech, we’re intentional about building an inclusive culture where everyone feels