Valtech
Posted 2mo ago

Senior Data Engineer

Valtech
XK or Bulgaria or Macedonia or Poland or Portugal or Ukraine
RemoteFull Time
Responsibilities
  • designing platforms
  • building pipelines
  • supporting ai
Requirements
  • Experienced building cloud-based data platforms and production-grade batch/streaming pipelines using Spark/Delta Lake
  • Python/SQL
  • Azure (Fabric) or AWS/GCP
  • Databricks/Snowflake
  • Strong data modeling, governance, and observability skills
Technical tools mentioned
Apache SparkDelta LakePythonSQLMicrosoft AzureMicrosoft FabricGCPAWSDatabricksSnowflakeAirflowdbtLakeflowTerraformARMCloudFormationKafka

Job description

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 across the organization. 

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 Microsoft Azure / Fabric (primary), GCP, AWS, Databricks, and Snowflake, with Azure experience being highly transferable to Fabric. You will collaborate closely with data scientists, ML engineers, and platform teams to ensure the data foundation supports production-grade, decision-oriented AI systems. 

Role responsibilities

Build & Data Platform Engineering 

Design and implement scalable data platforms and pipelines across cloud environments (Azure/Fabric, AWS, GCP, Databricks, Snowflake). 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), 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. 

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, Lakeflow, 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). 

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.  

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. 

Must have qualifications

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) 

Experience with one or more major cloud platforms, with preference for Microsoft Azure / Fabric, as well as AWS or GCP. Familiarity with modern data platforms such as Databricks and Snowflake is expected. 

Architecture & Systems Thinking 

Experience with lakehouse architectures and distributed data systems, and strong understanding of scalability, reliability, and performance considerations in data pipelines. 

Mindset 

Strong problem-solving skills focused on scalability and reliability, with a collaborative approach to working in cross-functional teams. Experience in Agile or consulting environments is beneficial. 

Nice to have qualifications  

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, ARM, 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. 

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. 

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 Valtech

Experience innovation providing digital transformation and consulting services.

Year founded
1993
Employees
6000
Organization type
Private
Latest investment
Private Equity (2021) — led by BC Partners
Headquarters
LU

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