Jobs at Dreamix

Design and implement batch and real-time data ingestion pipelines for a modernized large-scale data platform. Migrate Data Lake and Data Warehouse solutions, build scalable streaming and distributed data processing systems, optimize performance and reliability, support low-latency analytics, ensure data quality, and contribute to target architecture and technical decisions.

Own the software product backlog and release process, translating customer and stakeholder needs into prioritized epics and user stories. The role communicates product vision, manages release planning and dates, coordinates sprint commitments with development, QA, and DevOps teams, and validates delivered outcomes. It also requires stakeholder, vendor, and subject matter expert collaboration across international projects and industries.

Maintain Linux systems, Azure cloud infrastructure, containerized workloads, AKS, and web servers. Design and optimize CI/CD pipelines with Jenkins and GitLab, provision infrastructure through Terraform, and automate configuration using Ansible, Python, and Bash. Manage Git-based source control and implement Grafana and Prometheus monitoring. Collaborate with development and technical teams to improve reliability, deployment processes, automation, and engineering efficiency.

Lead the development, implementation, and optimization of AI solutions. Mentor team members and collaborate with cross-functional teams to drive innovation and ensure product deployment.

Drive end-to-end enterprise B2B sales for Dreamix: identify prospects in Western Europe and North America, execute multichannel outbound strategies, manage pipeline with CRM, close deals, and hand off accounts to Account Management while collaborating with Sales and Marketing.

Design, build, and operate production-grade Databricks-based data pipelines across medallion architecture. Define canonical data models, ensure performance, quality, governance, and reliability. Collaborate with data scientists and stakeholders, build data integrations/APIs, mentor engineers, and continuously optimize data infrastructure and workflows.

Lead engineering teams and projects end-to-end: define scope and metrics, support presales, align resources to deliver on time and budget, mentor engineers, drive technical strategy and continuous delivery improvements, and act as liaison across departments.

Lead design and build scalable, real-time data systems using Python, Kafka, and Spark. Architect streaming and backend infrastructure, deliver high-throughput solutions, lead projects end-to-end, collaborate cross-functionally, and mentor the data engineering team.