Hire(d) better.
Hire(d) better.
Hire(d) better.
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.
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:
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.
Our preferred platforms are AWS and Databricks (primary), with additional experience across Snowflake, Microsoft Azure / Fabric, and GCP considered a strong plus. Deep hands-on expertise with AWS data services (e.g., S3, Glue, EMR, Redshift, Kinesis, Lambda) and the Databricks Lakehouse Platform is a key requirement for this role.
You will collaborate closely with data scientists, ML engineers, and platform teams to ensure the data foundation supports production-grade, decision-oriented AI systems.
Build & Data Platform Engineering
Design and implement scalable data platforms and pipelines primarily on AWS and Databricks, with exposure to other cloud environments (Azure/Fabric, GCP, Snowflake) 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, 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 AWS AI/ML services and Databricks' MLflow and Unity Catalog where relevant.
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, 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.
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 AWS and Databricks environments.
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.
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)
Strong, demonstrable hands-on experience with AWS and Databricks is essential. Familiarity with other modern data platforms such as Snowflake, Azure/Fabric, 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.
Experience with GenAI and AI data systems (e.g., RAG pipelines, vector databases, LLM data preparation), as well as 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 to see how we’re creating a more equitable Valtech for all.
Beyond a competitive compensation package, we offer:
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.
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.
Will AI replace this role?
Check AI displacement risk, transferable skills, and career alternatives for Senior Data Engineer
The Senior Data Engineer position at Valtech is based in Sofia; Bulgaria; Kosovo; North Macedonia; Poland; Portugal; Ukraine. This is a hybrid position.
The Senior Data Engineer position at Valtech is a Internship role.
Key skills for the Senior Data Engineer position at Valtech include: machine learning, large language models, aws, snowflake, azure, google cloud, aws s3, redshift, airflow, dbt.