Develop, maintain, and evolve scalable data pipelines using Airflow as the primary orchestration tool;
Build robust integrations across multiple data sources, ensuring consistency, auditability, and data quality;
Work on data modeling and transformations for analytical and operational layers;
Ensure stability and observability of critical pipelines, reducing incidents and increasing platform reliability;
Contribute best practices for versioning, automation, testing, and architectural standards;
Collaborate with Software Engineering, Architecture, Product, and business teams to prioritize and deliver requirements;
Participate in technical decisions that impact the evolution of the data architecture;
Support the modernization of legacy processes and the continuous improvement of the platform.

Solid experience in Data Engineering, preferably in medium to large enterprise environments
Proven experience with Airflow (creating and maintaining DAGs)
Proficiency in Python for data ingestion and transformation
Hands-on experience building production-grade pipelines
Strong knowledge of relational and non-relational databases
Experience with ETL/ELT, data modeling, and query optimization
Familiarity with cloud environments, preferably Azure
Experience with Git, CI/CD, containerization, and automation
Ability to work with multiple stakeholders and translate business needs into consistent technical solutions
Experience with Kafka, Spark, or dbt
Knowledge of DataOps practices and observability
Experience with infrastructure as code
Cloud certifications

Senior Data Engineer

Key skills