Senior Data Engineer

• Design, build, and optimize modern cloud-based data platforms powering analytics, AI, and data products
• Develop reliable batch, streaming, and near-real-time pipelines for structured and unstructured data
• Build ingestion, transformation, and curation workflows
• Implement lakehouse architectures and medallion layering in Databricks or Fabric
• Deliver high-quality datasets for analytics, machine learning, causal modeling, optimization, GenAI, LLM, RAG, and agent-based systems
• Design data models and orchestrate automated workflows using Airflow, dbt, Databricks Workflows, Azure Data Factory, or equivalents
• Apply event-driven and streaming architectures and implement data governance, lineage, quality, access control, and observability
• Monitor data freshness, pipeline reliability, SLA adherence, performance, scalability, and cost efficiency
• Enable data serving layers, APIs, feature inputs, and analytical endpoints for downstream ML and AI systems
• Discover and refine business requirements through stakeholder analysis and experience-based recommendations
• Collaborate with data scientists, ML engineers, analysts, platform teams, and business stakeholders
• Support adoption of data products and contribute to data and AI ecosystem best practices

• 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
• Strong, demonstrable hands-on experience with either AWS data services and the Databricks Lakehouse Platform, or Microsoft Azure/Fabric and associated data tools
• Deep expertise in one cloud/platform track; Snowflake or GCP familiarity is a plus but not required
• Experience with lakehouse architectures and distributed data systems
• Strong understanding of scalability, reliability, and performance considerations in data pipelines
• Strong problem-solving skills and collaborative approach
• Experience in Agile or consulting environments is beneficial
• GenAI and AI data systems experience, CI/CD for data pipelines, Terraform or CloudFormation, Kafka, Spark optimization, advanced analytics/ML workloads, or large-scale analytics/data products are nice to have
• CV covering relevant experience and expertise

• 24 working days of paid vacation
• National holidays covered
• Sick leave (up to 20/year)
• Unpaid leave (up to 20/year)
• Medical insurance
• Multisport card OR Multikafeteria
• Maternity & paternity leave support
• Internal workshops & learning initiatives
• Professional certifications reimbursement
• Participation in professional local & global communities
• Growth Framework to manage expectations and define the steps to move towards the selected career
• Mentoring program with the ability to become a mentor or a mentee to grow to a higher position
• Progressive benefit packages—the longer you stay with the company, the more benefits you get
• Flexibility, with remote and hybrid work options (country-dependent)
• Career advancement, with international mobility and professional development programs
• Access to cutting-edge tools, training and industry experts
• Reasonable accommodations during the interview process