Sas to Databricks Migration Engineer AI

Description

We’re looking for a senior-minded Nearshore engineer who can turn SAS-based logic into robust, governed Databricks pipelines using Python, SQL, and modern CI/CD practices—while maintaining strict delivery discipline.

Required experience

• 4+ years of professional data or software engineering experience.
• Strong Python and SQL skills.
• Hands-on Databricks experience with Unity Catalog, Workflows, and Databricks Asset Bundles.
• Proficiency with GitLab CI/CD (pipelines, merge request workflows, and automated testing) and disciplined Git branching and code reviews.
• Clear written and spoken English for client-facing collaboration.
• Demonstrated ownership: scoping work, delivering outcomes, and proactively flagging risks without being prompted.

Focus areas

• Pipeline reliability, validation, and delivery of converted code.
• Parity checks between SAS and Databricks outputs.
• Deployment and governance using DABs + GitLab CI/CD + Unity Catalog.

Additional experience

• Spark and Delta Lake performance tuning.
• Data validation and reconciliation experience.
• Infrastructure-as-code or DAB-based deployment experience.
• SAS reading ability and exposure to healthcare data (plus experience with Azure) are valued.

How we work: We value clarity, accountability, and continuous improvement. We’ll expect you to communicate trade-offs, confirm assumptions early, and build trust through predictable delivery, thoughtful reviews, and transparent risk management.

Projects

At Improving, we deliver future-ready solutions across AI, Data, and Applications, modernizing legacy systems and building cloud-native platforms through collaborative, long-term partnerships. In this role, we’ll place a Nearshore engineer on a client-facing team focused on converting SAS code to Python and SQL running on Databricks. You’ll work with existing accelerators to accelerate delivery, then take ownership of end-to-end deliverables—from implementing reliable pipelines and validation logic, to deployment and operational troubleshooting. You’ll partner closely with client and internal stakeholders to ensure converted outputs are accurate, reproducible, and production-ready on Databricks, leveraging modern controls such as Unity Catalog, Databricks Asset Bundles, and GitLab CI/CD.

Functions

We’ll rely on you to drive the end-to-end conversion delivery from SAS inventories to validated Python/SQL outputs on Databricks, with a strong focus on pipeline reliability and data validation.

• Pipeline engineering: Build and run pipelines that process SAS inventories and produce converted outputs.
• Quality and parity validation: Validate converted code for parity against SAS outputs (e.g., row counts, checksums, schema, and data types).
• Deployment ownership: Own deployments through Databricks Asset Bundles (DABs) and GitLab CI/CD, ensuring repeatable releases.
• Databricks governance: Manage Unity Catalog objects, permissions, and promotion across environments.
• Operational excellence: Troubleshoot job failures and performance issues, and take preventive actions to improve pipeline stability.
• Performance tuning: Apply tuning techniques for Apache Spark and Delta Lake to meet reliability and execution-time expectations.
• Data reconciliation: Use data validation and reconciliation practices to ensure correctness and consistency.
• Infrastructure-as-code mindset: Implement DAB-based deployment patterns and support automated, testable delivery workflows.
• Client collaboration: Communicate progress, risks, and technical decisions clearly with client and partner stakeholders.

Benefits

• Contrato a largo plazo.
• 100% Remoto.
• Vacaciones y PTOs
• Posibilidad de recibir 2 bonos al año.
• 2 revisiones salariales al año.
• Clases de inglés.
• Equipamiento Apple.
• Plataforma de cursos en linea
• Budget para compra de libros.
• Budget para compra de materiales de trabajo
• mucho mas..

Desirable

• Experience converting SAS workflows to Python/SQL in production environments.
• Healthcare data exposure and familiarity with typical data quality and privacy expectations.
• Azure exposure and understanding of how cloud services fit into end-to-end delivery and operations.
• Deep experience optimizing Spark jobs (partitioning, caching strategies, skew handling) and Delta Lake (file sizing, compaction patterns).