Sas to Databricks Migration Engineer AI
Worldwide | Oct. 8, 2026
Report as Closed
Company:
Improving
Country: Worldwide
Salary: $40,800 - $54,000
Type: Remote
Employment: Full-time
Description: Improving is an IT services firm focused on AI, Data, and Applications. We modernize legacy systems, build cloud-native platforms, and deliver future-ready solutions through collaborative, long-term partnerships that drive measurable outcomes.
At Improving South America, we provide IT services to transform the perception of the IT professional. We focus on IT consulting, software development and agile training.
The company promotes an exceptional work culture based on teamwork, excellence and fun, with a focus on personal growth and shared rewards. By joining, the candidate will become part of a community that prioritizes open communication and strong long-term working relationships, supported by a structure of professional development and continuous learning.
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.
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 co
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