Data Engineer / Architect

Role description
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/fine‑tuning 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 cross‑functional communication
Other details
Bachelor’s or Master’s degree in Computer Science, Data Engineering, Information Systems, or related field