Data Engineer in Sibiu

Since 1998, we've been active in the Human Resources consulting market, providing regional coverage across four key areas of expertise: recruitment and selection, personnel leasing, assessment centers and consultancy. As leaders in Transylvania, we've expanded our reach to embrace a culture of continuous improvement, thereby strengthening our position in the Romanian and also regional market. This commitment underscores our dedication to evolve alongside the dynamic needs of our clients and the ever-changing landscape of the business environment. Our success stems from the professionalism of our services, the multidisciplinary expertise of our consulting team and our ongoing collaboration with those who rely on our consultancy services. Building long-term partnerships with clients across diverse industries such as IT&C, automotive, outsourcing, pharma, banking, FMCG and more, is our primary objective. Our commitment to client orientation, teamwork, flexibility, excellence, dedication and responsibility reflects our aim to bring added value to our services. Responsibilities : Implementing and designing scalable, optimized data pipelines for (pre-) processing ETL for machine learning models. Develop and maintain conceptual and logical data models using data modeling guidelines from the clients; Document and maintain business glossary in the enterprise data catalog solution; Evaluate business data models and physical data models for variances and discrepancies; Support project team in adopting business data models; Guide project team to map physical data models to business glossary. Knowledge/Experience :       For senior experience: Hands-on technologies and frameworks used in ML, like sklearn, MLFlow, TensorFlow; Building complex data pipelines e.g. ETL; Experience working in cloud environment, data cloud platforms (e.g. GCP); Understanding of code management repositories like GIT/SVN; Familiar with software engineering practices like versioning, testing, documentation, code review; Experience with Apache Airflow; Experience in setting up both SQL as well as noSQL databases; Experience with monitoring and observability (ELK stack); Deployment and provisioning with automation tools e.g. Docker, Kubernetes, Openshift, CI/CD; Knowledge of MLOps architecture and practices; Relevant work experience in ML projects; Knowledge of data manipulation and transformation, e.g. SQL; Setting up/troubleshoot SQL and NoSQL databases.      For medium experience: Design and Develop Data Pipelines: Create efficient and scalable data pipelines using GCP services such as Dataflow (Apache Beam), Dataproc (Apache Spark), and Pub/Sub; Data Storage Solutions: Implement and manage data storage solutions using GCP services such as BigQuery, Cloud Storage, and Cloud SQL; Data Analysis and Reporting: Optimize SQL queries for data analysis and reporting in BigQuery.