Senior Data Engineer
Sofia · Bulgaria · North Macedonia · Poland · Portugal · Hybrid · Posted Oct 6, 2026
At a glance
- Workplace
- Hybrid
- Location
- Sofia · Bulgaria · North Macedonia · Poland · Portugal
About Valtech
- 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.
About the role
- Valtech is 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.
- The role can be delivered on either of two core cloud ecosystems—AWS (with Databricks) or Azure/Fabric (with Databricks or native Fabric tooling)—matching your strongest platform.
Responsibilities
- Design and implement scalable data platforms and pipelines on AWS or Azure/Fabric with exposure to other environments like Snowflake or GCP.
- 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, as well as agent-based architectures and intelligent workflows.
- 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.
- Establish strong data observability, including monitoring of data freshness, pipeline reliability, and SLA adherence.
- Collaborate closely with data scientists, ML engineers, analysts, and business stakeholders to translate requirements into robust data solutions.
Requirements
- 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 and Azure Databricks.
- 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.
- Comfortable applying common sense thinking, detailed analysis, and experience-based recommendations to help derive and challenge business requirements.
Preferred qualifications
- 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 such as Kafka, Spark optimization, or advanced analytics and ML workloads including causal or experimentation platforms
- Experience building data products or large-scale analytics platforms
- Experience in Agile or consulting environments
Benefits
- Flexibility with hybrid work options
- 25 vacation days
- Co-subsidized transportation
- Multisport cards
- Premium health insurance
- 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 and discussions
- Cozy office space
- Exciting team events and company gatherings
Workplace
- Hybrid work options available.
Skills
- Apache Spark
- Delta Lake
- Python
- SQL
- AWS
- Databricks
- Azure
- Snowflake
- GCP
- Airflow
- Dbt
- Azure Data Factory
Show all 14 skills
- Terraform
- Kafka
