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