Must-Have: 5+ years of proven experience in a Data Engineering role, with a strong background in data architecture. Exceptional proficiency in SQL and Python for data manipulation, scripting, and pipeline automation. Deep hands-on experience with modern data orchestration and transformation tools, specifically Airflow and dbt. Extensive experience managing and optimizing cloud data platforms such as BigQuery / Databricks / Snowflake. Demonstrated experience in data analysis, with the ability to act as a Data Analyst to query data, build reports, and extract actionable insights. Practical experience designing or supporting data infrastructure for an agentic environment or AI/LLM-driven applications. Strong attention to detail, analytical mindset, and excellent communication skills. Experience of one or more of these technologies: Kafka, Kubernetes, ArgoCD, Terraform, Debezium. Understanding of data modeling principles: dimensional modeling, fact/dimension tables, slowly changing dimensions Experience with Git workflows: branching, PRs, code reviews, and CI/CD for data pipelines. Ownership mindset: ability to debug production issues, drive projects to completion independently
