Senior Data Engineer

Strong experience with Apache Spark, Python, and SQL

Hands-on experience with Azure Cloud, Azure Databricks, Azure Data Factory, Azure Data Lake, Azure Synapse Analytics, and Azure Data Explorer

Proven experience in building and maintaining ETL/ELT processes

Practical experience with Databricks, Delta Live Tables (DLT), and Unity Catalog

Understanding of Lakehouse architecture, data governance, security, compliance, and operational excellence

Familiarity with CI/CD, Azure DevOps, infrastructure automation, monitoring, and logging

Strong analytical and problem-solving skills

Ability to work independently with a high level of ownership and accountability

Proactive approach to identifying risks, blockers, and improvement opportunities

Strong communication and stakeholder management skills across technical and business teams

Experience working in dynamic environments with frequently changing priorities and cross-functional teams

Ability to make pragmatic decisions and deliver results despite incomplete requirements

Adaptability, resilience, self-organization, and ability to independently drive initiatives and propose practical solutions

English proficiency at minimum B2+ level

Offer:

• Private medical care
• Co-financing for the sports card
• Constant support of dedicated consultant
• Employee referral program

,[Design, develop, and maintain scalable data solutions across Azure Synapse, Azure Databricks, and Data Lakehouse environments, Implement and optimize ETL/ELT processes for ingesting, transforming, and serving data across multiple business domains, Develop and maintain data processing solutions using Python, SQL, and Apache Spark, Support migration and modernization initiatives, including Unity Catalog adoption, platform upgrades, and data platform transformations, Build and maintain Databricks workflows, Delta Live Tables (DLT), metadata-driven frameworks, and orchestration processes, Collaborate with architects, analysts, and business stakeholders to translate business requirements into technical solutions, Ensure data quality, reliability, performance, and security across the data platform, Troubleshoot production incidents, perform root cause analysis, and implement corrective actions, Contribute to CI/CD, infrastructure automation, monitoring, logging, and operational best practices, Optimize data pipelines, storage structures, and processing workloads for efficiency and cost reduction, Participate in solution and architecture reviews while promoting engineering standards and governance principles, Support knowledge sharing, mentoring, and technical development within the team, Collaborate with Data Architects and platform teams to align solutions with enterprise data architecture, Lakehouse principles, and governance requirements, Support reporting, analytics, and AI/ML initiatives by providing reliable and scalable data foundations] Requirements: Spark, Databricks, Azure Databricks, Azure Data Lake, Python