Senior Data Engineer

About this role

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. Beyond technical delivery, we want someone genuinely curious about the business problems behind the data, who applies common sense, detailed analysis, and experience-based recommendations to help shape business requirements, not just implement them as given.

You will work on scalable batch, streaming, and near-real-time pipelines, delivering high-quality curated datasets with robust governance, security, and observability. You will also support AI and GenAI systems, enabling pipelines for machine learning, causal modeling, and LLM-powered applications such as RAG and agent-based systems, and collaborate closely with data scientists, ML engineers, and platform teams.

Platforms

Our work is centered on AWS with Databricks, with growing demand on GCP, including cloud migration programs. Azure/Fabric experience is also valued. We are looking for engineers with deep expertise in one cloud who are T-shaped: comfortable working across clouds and keen to broaden their platform experience. You’ll be staffed on projects that fit your strongest platform, with opportunities to expand.

Responsibilities

Build & Data Platform Engineering

• Design and implement scalable data platforms and pipelines, primarily on AWS and Databricks, and on GCP or Azure/Fabric where projects require.

• Develop reliable batch, streaming, and near-real-time pipelines using Spark and Delta Lake.

• Build ingestion, transformation, and curation workflows for structured and unstructured data.

• Implement lakehouse patterns and medallion layering (bronze, silver, gold), ensuring reusable, scalable systems aligned with enterprise needs.

• Contribute to cloud-to-cloud migrations, including re-platforming pipelines and data models.

Enable AI, GenAI & Data Products

• Deliver model-ready, production-grade datasets for analytics, ML, causal modeling, and optimization.

• Build data pipelines for GenAI use cases (LLMs, RAG, vector-based data flows) and agent-based architectures.

• Leverage Databricks MLflow and Unity Catalog, or equivalent tooling on other platforms.

Data Modeling, Orchestration & Automation

• Design logical and physical data models for analytical and operational use cases.

• Orchestrate workflows with Databricks Workflows, Airflow, dbt, or equivalents (e.g., Azure Data Factory, Cloud Composer), with a focus on automation, reliability, and maintainability.

Architecture, Governance & Observability

• Apply event-driven and streaming architecture patterns.

• Ensure data governance, lineage, quality, and access control (RBAC/ABAC) using Unity Catalog, AWS Lake Formation, or equivalents (Microsoft Purview, GCP Dataplex).

• Establish observability: data freshness, pipeline reliability, and SLA adherence.

Data Serving, Integration & Optimization

• Enable serving layers (APIs, feature inputs, analytical endpoints) for downstream ML and AI platforms.

• Continuously optimize pipelines and infrastructure for performance, scalability, and cost.

Requirements Discovery & Business Partnership

• Ask the right questions to understand not just what stakeholders want, but why.

• Surface gaps and better alternatives rather than executing a brief as written.

Collaboration

• Work closely with data scientists, ML engineers, analysts, and business stakeholders.

• Support adoption of data products and contribute to best practices.

Must-have qualifications

Technical skills

• Strong hands-on experience with Apache Spark and Delta Lake.

• Strong Python and SQL.

• Proven experience building batch and streaming pipelines and production-grade data platforms.

• Solid understanding of data modeling, data quality, and governance.

Cloud & platforms (key requirement)

• Strong, demonstrable hands-on experience with the Databricks Lakehouse Platform, ideally on AWS (e.g., S3, Glue, EMR, Redshift, Kinesis, Lambda).

• Deep expertise in at least one major cloud (AWS, GCP, or Azure/Fabric), plus demonstrable openness and ability to work across clouds. Candidates with a strong Azure/Fabric background (Data Factory, Synapse, Fabric Lakehouse, Azure Databricks) are encouraged to apply, provided they are keen to work in other clouds.

• GCP experience (e.g., BigQuery, Dataflow, Pub/Sub, Cloud Storage, Dataproc) is highly valued, especially in migration contexts.

Architecture & systems thinking

• Experience with lakehouse architectures and distributed systems.

• Strong understanding of scalability, reliability, and performance.

Mindset

• Naturally curious, with strong problem-solving skills and a collaborative approach in cross-functional teams.

• Comfortable challenging business requirements rather than taking them at face value.

• Adaptable and eager to work across platforms. Agile or consulting experience is beneficial.

Nice to have

• Hands-on experience across more than one cloud, or cloud migration experience (e.g., Azure to GCP).

• Snowflake experience.

• GenAI and AI data systems (RAG pipelines, vector databases, LLM data…