Senior Data Engineer

• Design and implement scalable data platforms and pipelines across cloud environments
• develop reliable batch, streaming, and near-real-time pipelines
• implement modern data architectures including lakehouse patterns and medallion layering
• deliver high-quality datasets supporting analytics, machine learning, causal modeling, and optimization systems
• enable data pipelines for GenAI use cases
• design scalable logical and physical data models
• orchestrate workflows using tools such as Airflow, dbt, Lakeflow
• apply modern architecture patterns including event-driven and streaming architectures
• ensure adherence to best practices in data governance, lineage, quality, and access control
• establish strong data observability
• enable data serving layers
• continuously monitor and optimize pipelines and infrastructure
• work closely with data scientists, ML engineers, analysts, and business stakeholders.

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. 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. Experience with lakehouse architectures and distributed data systems, and strong understanding of scalability, reliability, and performance considerations in data pipelines. Strong problem-solving skills focused on scalability and reliability, with a collaborative approach to working in cross-functional teams.

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.

• 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
• Mentoring program
• Flexibility, with remote and hybrid work options
• Career advancement, with international mobility and professional development programs
• Learning and development, with access to cutting-edge tools, training and industry experts