Senior Data Engineer

About the job

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. 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 preparation).

- CI/CD for data pipelines and infrastructure-as-code (Terraform, CloudFormation).

- Streaming technologies (e.g., Kafka), Spark optimization, or ML/experimentation platforms.

- Experience building data products or large-scale analytics platforms.

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 to see how we’re creating a more equitable Valtech for all.

The benefits

Beyond a competitive compensation package, we offer:

- Flexibility, with hybrid work options and 25 vacation days for a healthy work-life balance.

- Co-subsidized transportation & Multisport cards.

- Premium health insurance for fast and easy access to top healthcare services.

- Training policy for technical and other skills-related events, courses and certifications.

- Personal career development roadmap guided by performance evaluations.

- Self-care program offering psychological consultations & discussions for you and the team.

- Cozy office space designed for comfort and productivity.

- Exciting team events and company gatherings.

Your application process

Once you apply, our Talent Acquisition team will review your application. Your CV should cover key information on relevant experiences and expertise. We do not require information such as age, gender, marital status, or a headshot in your application. We review all candidates based on skills, experience, and potential.

⚠️ Beware of recruitment fraud!

We are committed to inclusion and accessibility. If you need reasonable accommodations during the interview process, please either indicate it in your application or let your Talent Partner know.