Senior Data Engineer

Poland - Remote · Bulgaria · North Macedonia · Poland · Portugal · Posted Oct 6, 2026

At a glance

Valtech is the experience innovation company that exists to unlock a better way to experience the world. By blending crafts, categories, and cultures, we help brands unlock new value in an increasingly digital world.
At the intersection of data, AI, creativity, and technology, we drive transformation for leading organizations, including L’Oréal, Mars, Audi, P&G, Volkswagen Dolby, and more.
At Valtech, we don’t just talk about transformation. We make it happen. Our people are the heart of our success, and we foster a workplace where everyone has the support to thrive, grow and innovate.
Are you ready to create what’s next? Join us.

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.
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.

Design and implement scalable data platforms and pipelines on AWS (with Databricks) or Azure/Fabric (with Databricks or native Fabric tooling).
Develop reliable batch, streaming, and near-real-time pipelines using technologies such as Spark and Delta Lake.
Build ingestion, transformation, and curation workflows for both structured and unstructured data.
Implement modern data architectures including lakehouse patterns and medallion layering (bronze, silver, gold) within Databricks or Fabric.
Deliver high-quality datasets that support analytics, machine learning, causal modeling, and optimization systems.
Enable data pipelines for GenAI use cases, including LLMs, RAG pipelines, and vector-based data flows.
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.
Ensure adherence to best practices in data governance, lineage, quality, and access control (RBAC/ABAC) using tools such as Unity Catalog, AWS Lake Formation, or Microsoft Purview.
Collaborate closely with data scientists, ML engineers, analysts, and business stakeholders to translate requirements into robust data solutions.

Strong hands-on experience with Apache Spark and Delta Lake.
Strong programming skills in Python and SQL.
Proven experience building batch and streaming data pipelines and production-grade data platforms.
Solid understanding of data modeling, data quality, and governance principles.
Track A: Strong, demonstrable hands-on experience with AWS data services and the Databricks Lakehouse Platform, OR Track B: Strong, demonstrable hands-on experience with Microsoft Azure / Fabric.
Experience with lakehouse architectures and distributed data systems.
Strong understanding of scalability, reliability, and performance considerations in data pipelines.
Naturally curious with strong problem-solving skills focused on scalability and reliability, and a collaborative approach.
Comfortable applying common sense thinking, detailed analysis, and experience-based recommendations to help derive and challenge business requirements.

Familiarity with other modern data platforms such as Snowflake or GCP
Deep expertise in both tracks — AWS and Azure/Fabric, across Databricks and native Fabric tooling
Experience with GenAI and AI data systems, such as RAG pipelines, vector databases, and LLM data preparation
Experience with CI/CD for data pipelines
Experience with infrastructure-as-code tools such as Terraform or CloudFormation
Additional exposure to streaming technologies like Kafka, Spark optimization, or advanced analytics and ML workloads
Experience building data products or large-scale analytics platforms
Experience in Agile or consulting environments

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