Databricks + Azure Data Engineer

We're Hiring: Databricks + Azure Data Engineer

Location: LATAM based (Remote)

Are you a data engineering pro who lives and breathes Azure Databricks and the Lakehouse? We're looking for an experienced Databricks + Azure Developer to design, build, and optimize scalable data engineering solutions on Azure. If you love building high-performance ETL/ELT pipelines and implementing Medallion architecture, let's talk!

What You'll Do

• Design, develop, and maintain data pipelines using Azure Databricks and PySpark

• Build and orchestrate ETL/ELT workflows with Azure Data Factory (ADF)

• Develop ingestion & transformation pipelines using ADLS Gen2, Delta Lake, and Spark SQL

• Implement Medallion architecture (Bronze/Silver/Gold), incremental loads, CDC, MERGE, and SCD Type 1/2

• Optimize Spark jobs via partitioning, caching, file-size optimization, and performance tuning

• Configure and maintain Databricks Workflows/Jobs, clusters, and workspaces

• Build data quality checks, validation, error handling, monitoring, and reconciliation

• Leverage Unity Catalog for governance, lineage, RBAC, and secure data access

• Integrate Databricks with Azure services (ADLS, Azure SQL, APIs, JDBC)

• Implement CI/CD with Azure DevOps & Git; automate IaC with Terraform or ARM/Bicep

• Troubleshoot production pipelines, run root-cause analysis, and ensure SLA compliance

• Collaborate with architects, analysts, and BI teams in an Agile environment

What You Bring

• 7–10+ years in Data Engineering or Azure cloud data development

• Strong hands-on with Azure Databricks, PySpark, Spark SQL, and Delta Lake

• Proven experience building scalable ETL/ELT pipelines with ADF

• Solid grasp of ADLS Gen2, Lakehouse architecture, and distributed data processing

• CI/CD (Azure DevOps), Git, YAML pipelines, and IaC (Terraform/ARM/Bicep)

• Strong SQL skills with performance-tuning expertise

• Experience in Agile/Scrum environments

Nice to Have

• Databricks platform administration (clusters, jobs, workspace config)

• Unity Catalog, data governance, lineage & RBAC know-how

• Databricks integration with APIs, JDBC sources & external systems

• Familiarity with Datadog/Grafana and cost optimization