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 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. Beyond technical delivery, we're looking for someone genuinely curious about the business problems behind the data — someone who can apply common sense thinking, detailed analysis, and experience-based recommendations to help derive and shape business 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. 

This role can be delivered on either of our two core cloud ecosystems — AWS (with Databricks) or Azure/Fabric (with Databricks or native Fabric tooling) — and you'll be staffed on projects that match your strongest platform. You don't need experience in both. Additional experience across Snowflake or GCP is considered a strong plus. 

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 on whichever of our two core ecosystems matches your strengths — AWS (with Databricks) or Azure/Fabric (with Databricks or native Fabric tooling) — with exposure to other environments (Snowflake, GCP) considered a plus. 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) within Databricks or Fabric, 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 — leveraging Databricks' MLflow and Unity Catalog, or the equivalent Azure/Fabric and Azure ML tooling, depending on the platform you work in. 

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, Databricks Workflows, Azure Data Factory, 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), using tools such as Unity Catalog and AWS Lake Formation, or Microsoft Purview, depending on the platform in use. 

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 across our core cloud ecosystems. 

Requirements Discovery & Business Partnership 

Bring genuine curiosity to every engagement — ask the right questions to understand not just what stakeholders are asking for, but why. Apply common sense thinking, detailed analysis, and experience-based recommendations to help derive and refine business requirements, surfacing gaps or better alternatives where they exist rather than simply executing a brief as written. 

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) — choose your track: 

• Track A: Strong, demonstrable hands-on experience with AWS data services (e.g., S3, Glue, EMR, Redshift, Kinesis, Lambda) and the Databricks Lakehouse Platform 

• Track B: Strong, demonstrable hands-on experience with Microsoft Azure / Fabric (e.g., Data Factory, Synapse, Fabric Lakehouse, Azure Databricks) 

You only need deep expertise in one track — we treat both as equally weighted core platforms and will align you to projects based on where your experience is strongest. Familiarity with other modern data platforms such as Snowflake or GCP is a plus but not required. 

Architecture & Systems Thinking 

Experience with lakehouse architectures and distributed data systems, and strong understanding of scalability, reliability, and performance considerations in data pipelines. 

Mindset 

Naturally curious, with strong problem-solving skills focused on scalability and reliability, and a collaborative approach to working in cross-functional teams. Comfortable applying common sense thinking, detailed analysis, and experience-based recommendations to help derive and challenge business requirements rather than taking them at face value. Experience in Agile or consulting environments is beneficial. 

Nice to have qualifications 

Deep expertise in both tracks — AWS and Azure/Fabric, across Databricks and native Fabric tooling — is a strong plus, though not required, as is experience with GenAI and AI data systems (e.g., RAG pipelines, vector databases, LLM data preparation), CI/CD for data pipelines, and infrastructure-as-code tools such as Terraform 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 supported to grow, thrive and achieve their goals. No matter your background, you belong here. Explore our Diversity & Inclusion site