Sas to Databricks Migration Engineer AI
# Sas to Databricks Migration Engineer AI **Improving** · Remote · `Remote` 🕒 **Статус:** *Опубликовано: сегодня* · *Источник: Get on Board (LATAM)* --- ### Top Skills & Match 🎯 **Ключевой стек роли:** `[Python]` `[Azure]` `[Операционная эффективность]` `[Управление рисками]` --- ### About the Role 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. 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. - 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).
Наблюдалась 2026-10-07, впервые 2026-10-07, источник — Get on Board (LATAM).