Data Engineer

We are looking for a Senior Data Engineer with at least 4 years of proven experience in data engineering to join a distributed team embedded within a software company, with the goal of simplifying the data architecture, raising quality standards, and accelerating the delivery of data products ready for analytics, reporting, and artificial intelligence. Our client is a software company offering real engineering challenges at scale, operating within a cross-functional environment that spans Data Engineering, BI, and product teams. Requirements - Demonstrable experience of at least 4 years as a Data Engineer, with strong command of SQL and Python, and the ability to design transformations that are clear, efficient, and testable. - Hands-on proficiency with the technology stack: Databricks, Airflow, dbt, and Snowflake. - Strong analytical skills to tackle complex problems, propose alternatives, and communicate technical decisions clearly. Product mindset and a focus on continuous improvement. - Knowledge of data modeling, version control, CI/CD, observability, and data quality. - Highly valued: experience applying generative AI or machine learning to accelerate engineering tasks or enable new data products. Responsibilities - Orchestrate processes with Airflow, incorporating monitoring, alerting, retries, and quality checks. - Design and evolve analytical models with dbt, applying best practices in modularity, testing, documentation, and lineage. - Contribute to defining layered architectures and data contracts that facilitate consumption by BI, analytics, and AI. - Optimize performance, cost, and reliability of pipelines, helping identify technical debt and opportunities for simplification. - Work with business, BI, and platform stakeholders to translate real needs into sustainable data solutions. - Explore and apply AI use cases that improve development, quality, or documentation. What we offer - Permanent contract. - 100% remote work. - Social perks. - Career development and personalized training. - Real challenges in architecture, quality, automation, and scalability. - Cross-functional way of working alongside Data Engineering, BI/Reporting, and other product and technology teams.