Data Engineer

• Designing, building, and maintaining scalable data environments and pipelines
• Developing and managing data architectures (such as data lakehouses, data warehouses, and platforms), along with associated CI/CD and ETL/ELT pipelines
• Integrating and analyzing data from multiple sources (both batch and streaming) to generate actionable insights and reports
• Collaborating within multidisciplinary teams, translating business needs into technical solutions, and keeping stakeholders informed on progress
• Continuously improving and innovating data solutions

• Cloud & Data Platforms: Experience with AWS or Microsoft Azure (including services such as Databricks, Event Hubs, and Data Factory), as well as platforms like Snowflake and Databricks
• Infrastructure-as-Code & Orchestration: Proficiency with Terraform, Kubernetes (e.g., AKS or EKS), Helm, and tools such as Argo Workflows or ArgoCD
• CI/CD & Automation: Familiarity with GitHub Actions, Azure DevOps/TFS, and Docker for containerization and automation
• Data Engineering Tools: Strong programming skills in Python, Java, Kotlin, Go, and/or SQL for data processing. Experience with DBT for data transformations and modeling (e.g., Data Vault) and Apache Airflow for workflow orchestration
• Monitoring & Observability: Experience using Prometheus and Grafana to monitor and visualize data pipelines
• Messaging & Streaming: Knowledge of event streaming technologies such as Kafka (and optionally Azure Event Hub)
• Machine Learning (Optional): Experience with MLflow for model management and a basic understanding of machine learning concepts is a plus

Experience with MLflow for model management and a basic understanding of machine learning concepts