Strong proficiency in Databricks platform: Delta Lake, Spark SQL, PySpark, Unity Catalog, MLflow, and Databricks Workflows; - Deep expertise in data modeling (dimensional, data vault, medallion/lakehouse architectures); - Experience building ETL/ELT pipelines using Databricks, Apache Spark, or comparable data engineering tools; - Proficiency in SQL and Python for data transformation, pipeline orchestration, and automation; - Understanding of data governance principles: data cataloging, lineage, quality monitoring, access control, and metadata management; - Familiarity with cloud data platforms (Azure Data Lake Storage, Azure Synapse, AWS S3/Glue, or similar); - Understanding of AI/ML data requirements: feature engineering, RAG data preparation, embedding storage, and LLM training/finetuning data pipelines; - Experience integrating data from enterprise systems: ServiceNow, Workday, Active Directory, CMDB, Jira; - Knowledge of data privacy and compliance standards (GDPR, LGPD) and security best practices for data platforms; - Comfortable with CI/CD pipelines for data (Databricks Asset Bundles, Terraform, GitHub Actions, Azure DevOps); - Strong skills in documentation, data storytelling, and crossfunctional communication

-5+ years' experience in database architecture, data engineering, or data platform design; - 2+ years' handson experience with Databricks (Delta Lake, Unity Catalog, Spark SQL, MLflow) strongly preferred; - Experience designing enterprise data lakehouse or data warehouse architectures at scale; - Familiarity with AI/ML data pipelines, LLM data requirements, and Agentic AI data consumption patterns is a plus; - Databricks Certified Data Engineer or equivalent cloud data certification preferred.