O que a vaga pede
Accelerate your development and exposure to high‑performance data platforms and cloud infrastructure. Join Sedona Digital, a fast‑growing scale‑up with the ambition to be recognised as one of the leading technology companies in Romania. Our global client base needs builders, engineers who enjoy designing and implementing scalable data platforms, have deep expertise in cloud data technologies, and take pride in delivering reliable, well‑governed solutions. At Sedona, we: Obsess about our customers Build robust, scalable technical solutions Create an open, collaborative culture Invest in learning and long‑term careers We are looking for a Senior Data Engineer with strong experience in AWS-based data platforms, Snowflake, data integration, and end-to-end pipeline development. The ideal candidate is comfortable working independently, making sound technical decisions, taking ownership of client data processes, and supporting the delivery of modern data and AI solutions. The role combines cloud data engineering with responsibility for established data-processing workflows, including a large-scale SAS-based healthcare claims process. The successful candidate will contribute across AWS, Snowflake, data modelling, automation, analytics, and AI initiatives while helping improve the reliability and maintainability of existing solutions. Responsibilities Design, build, and maintain scalable data pipelines and integration workflows using AWS, Snowflake, SQL, and Python. Manage data ingestion from APIs, databases, SFTP locations, and file-based sources. Build and optimise data models, transformations, and data-quality controls. Take ownership of recurring client processes, including the monthly SAS-based claims workflow. Monitor and troubleshoot pipelines, batch processes, and production data environments. Apply security, governance, access-control, and credential-management best practices. Improve pipeline resilience through effective monitoring, error handling, restartability, and recovery. Optimise data workloads for performance, scalability, and cost. Develop reusable components and patterns for ingestion, transformation, validation, and monitoring. Prepare governed and trusted datasets for analytics, reporting, AI, and machine-learning use cases. Collaborate with clients and internal stakeholders to translate business requirements into practical technical solutions. Identify opportunities to modernise legacy processes, reduce manual effort, and improve operational