Data Platform Engineer (102-08SENG-01)

This role converts a large Azure Data Factory estate into Databricks workflows on AWS. The scope for year one: 2,094 ADF pipelines to migrate — built as reusable templates rather than one-by-one — 9,859 pipeline activities to translate (some map directly, others need rewriting as Lambda or Step Functions), 471 Spark dataflows to move onto Databricks on AWS, and 4 Databricks workspaces (Dev, QA, Pre-prod, Prod) to rehost, including notebook paths and Unity Catalog rewiring. This is a regulated environment, so reconciling migrated data against source systems is part of the definition of done, not an afterthought. Requirements What you will do • Convert Azure Data Factory pipelines into Databricks workflows on AWS, building reusable templates rather than migrating one at a time. • Rehost Databricks workspaces onto AWS and migrate ADLS Gen2 storage to S3. • Rewrite ADF Web Activities as Lambda functions or Step Functions tasks, and replace ADF-specific scaling with native Databricks mechanisms. • Build and tune PySpark transformations for production data volumes. • Replace Azure Synapse Serverless with Databricks SQL Warehouse. • Reconcile migrated data against source systems as part of the definition of done. Required • Production experience with Databricks: workspaces, jobs and workflows. The central skill for this role. • Strong Spark and PySpark experience for real data volumes, including tuning. • Production-grade Python. • Experience building or migrating Azure Data Factory pipelines, with a solid understanding of the ADF activity model. • AWS data services: S3, Glue, Athena, Lambda and Step Functions. • Advanced SQL, including reading and reasoning about stored procedures. • Professional written and spoken English. Nice to have Delta Lake, Unity Catalog, Azure Synapse, Terraform, Airflow/MWAA, dbt, Kafka, Databricks certification, data modeling, Great Expectations, SAS/analytics platform integration, CRM data. Engagement details • Full-time • 100% remote • Open to candidate from all LATAM Highlights Databricks, PySpark, Python, Azure Data Factory, AWS (S3, Glue, Athena, Lambda, Step Functions), SQL, Delta Lake, Unity Catalog, Azure Synapse, Terraform, Airflow/MWAA, dbt, Kafka Originally posted on Himalayas