Senior Data Engineer

Valtech is an experience innovation company that helps brands unlock new value in a digital world by blending crafts, categories, and cultures. They drive transformation for leading organizations including L’Oréal, Mars, Audi, P&G, Volkswagen, and Dolby.

Technical skills:

• 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.

Cloud & Platforms (choose one track):

• Track A: Hands-on experience with AWS data services (e.g., S3, Glue, EMR, Redshift, Kinesis, Lambda) and the Databricks Lakehouse Platform.
• Track B: Hands-on experience with Microsoft Azure / Fabric services (e.g., Data Factory, Synapse, Fabric Lakehouse, Azure Databricks).

Architecture & Systems Thinking:

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

Mindset:

• Naturally curious with strong problem-solving skills focused on scalability and reliability.
• Collaborative approach in cross-functional teams.
• Comfortable applying detailed analysis and experience-based recommendations to derive and challenge business requirements.
• Experience in Agile or consulting environments is beneficial.

Nice to have qualifications:

• Deep expertise in both AWS and Azure/Fabric, across Databricks and native Fabric tooling.
• Experience with GenAI and AI data systems (e.g., RAG pipelines, vector databases, LLM data preparation).
• Experience with CI/CD for data pipelines and infrastructure-as-code tools such as Terraform or CloudFormation.
• Exposure to streaming technologies like Kafka, Spark optimization, or advanced analytics and ML workloads.
• Experience building data products or large-scale analytics platforms.

Build & Data Platform Engineering:

• 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 like Spark and Delta Lake.
• Build ingestion, transformation, and curation workflows for structured and unstructured data.
• Implement modern data architectures including lakehouse patterns and medallion layering in Databricks or Fabric.

Enable AI, GenAI & Data Products:

• Deliver high-quality datasets supporting analytics, machine learning, causal modeling, and optimization systems.
• Enable data pipelines for GenAI use cases (LLMs, RAG pipelines, vector-based data flows) and agent-based architectures.
• Leverage Databricks' MLflow, Unity Catalog, or equivalent Azure/Fabric and Azure ML tooling.

Data Modeling, Orchestration & Automation:

• Design scalable logical and physical data models for analytical and operational use cases.
• Orchestrate workflows using Airflow, dbt, Databricks Workflows, Azure Data Factory, or equivalent tools.
• Focus on automation, reliability, and maintainability of end-to-end pipelines.

Architecture, Governance & Observability:

• Apply modern architecture patterns including event-driven and streaming architectures.
• Ensure best practices in data governance, lineage, quality, and access control (RBAC/ABAC) using tools like Unity Catalog, AWS Lake Formation, or Microsoft Purview.
• Establish strong data observability including monitoring data freshness, pipeline reliability, and SLA adherence.

Data Serving, Integration & Optimization:

• Enable data serving layers (APIs, feature inputs, analytical endpoints) supporting downstream systems including ML and AI platforms.
• Monitor and optimize pipelines and infrastructure for performance, scalability, and cost efficiency.

Requirements Discovery & Business Partnership:

• Engage with stakeholders to understand business requirements deeply and refine them.
• Provide experience-based recommendations to improve business solutions.

Collaboration:

• Work closely with data scientists, ML engineers, analysts, and business stakeholders.
• Support adoption of data products and contribute to best practices across data and AI ecosystems.

This position is based in Poland. Candidates are reviewed based on skills, experience, and potential. The employer is committed to inclusion, accessibility, and warns about recruitment fraud. Applicants can request reasonable accommodations during the interview process.