Note: The job is a remote job and is open to candidates in USA. CoSourcing Partners is an enterprise-AI and IT services company. The SQL Server to Snowflake Migration Consultant will provide hands-on expertise converting SQL Server workloads to Snowflake, troubleshooting migration issues, validating data and reporting outputs, supporting ETL pipelines, tuning performance, and transferring knowledge to the internal data team.
Responsibilities
- Beginning immediately and throughout the engagement, independently analyze existing Microsoft SQL Server queries and workloads and convert them into accurate, supportable, and efficient Snowflake-compatible SQL. Preserve required business and data logic while appropriately addressing differences between SQL Server and Snowflake rather than relying on simple syntax substitution. Troubleshoot conversion problems and work through complex queries until the resulting Snowflake workloads produce the required outputs. AI-assisted code conversion or analysis may be used where permitted, but all converted SQL must be independently reviewed, tested, and validated by the consultant
- Throughout the engagement, investigate and resolve technical problems that could delay or increase the risk of the SQL Server-to-Snowflake migration. Diagnose issues involving SQL behavior, data differences, ETL processing, dependencies, performance, or interactions among migration components, and determine appropriate corrective actions. Collaborate with members of the data team when issues cross system or functional boundaries and communicate material risks clearly
- As workloads are converted, systematically validate data and results between the existing SQL Server environment and Snowflake to determine whether migrated solutions produce the expected business outcomes. Investigate discrepancies, distinguish expected platform differences from actual migration defects, and work with appropriate team members to correct identified issues. Support validation of downstream reports and dashboards, including Power BI where applicable, to ensure reporting behavior remains consistent with expected requirements
- During migration testing and stabilization, identify Snowflake queries or workloads that require performance improvement and apply appropriate tuning techniques without compromising data accuracy or required business logic. Support ETL pipeline troubleshooting and optimization as required, including dependencies involving Azure Data Factory where applicable, and help resolve reliability issues that could affect migration execution. Document material changes and collaborate with internal team members so improvements remain understandable and supportable after the engagement
- At the beginning of the engagement, review assigned SQL Server workloads, migration status, known defects, dependencies, validation requirements, and outstanding conversion priorities with the internal data team. Identify technically complex or high-risk queries requiring immediate attention and understand how their outputs affect downstream ETL processes, reporting, or dashboards. Establish an execution sequence based on migration priorities and dependencies rather than treating all conversions as equivalent
- Throughout the engagement, personally convert existing Microsoft SQL Server queries and workloads into Snowflake-compatible SQL while preserving required business logic and expected results. Identify differences in syntax, functions, data handling, query behavior, and platform capabilities that require redesign or modification rather than literal translation. Test converted queries and troubleshoot failures until the Snowflake implementation operates as required
- As converted workloads become available for testing, compare SQL Server and Snowflake outputs to identify missing data, incorrect calculations, unexpected record counts, transformation differences, or other reconciliation issues. Trace discrepancies through queries, transformations, source data, and downstream dependencies until the cause can be identified and corrected or appropriately explained. Maintain sufficient validation evidence so internal team members can understand how migrated workloads were tested
- During the migration, provide hands-on troubleshooting for ETL processes and dependencies that prevent converted Snowflake workloads from operating reliably. Investigate pipeline failures, transformation issues, sequencing problems, data dependencies, and related technical defects, including Azure Data Factory dependencies when applicable. Coordinate with the broader data team when resolution requires changes outside the consultant's direct area of ownership
- As converted workloads move toward completion, evaluate Snowflake query performance and address significant performance problems that could affect production usability or migration success. Apply appropriate Snowflake optimization techniques, retest affected workloads, and verify that performance improvements do not alter required data or business logic. Support final technical validation of assigned workloads before they are considered migration-ready
- During the later stages of workload migration, validate downstream reports and dashboards affected by the SQL Server-to-Snowflake transition, including Power BI outputs where applicable. Investigate reporting differences that may originate from converted queries, data transformations, ETL processes, or downstream reporting logic and help resolve identified defects. Document important conversion patterns, troubleshooting findings, technical decisions, and lessons learned and transfer this knowledge to internal staff where appropriate
- Within the first 90–180 days, or within the available engagement period, take ownership of identifying how AI and automation tools can safely accelerate SQL conversion, query analysis, migration troubleshooting, data reconciliation, testing, documentation, and Snowflake performance analysis. Evaluate tasks that can be streamlined or improved, lead appropriate pilots, and incorporate useful capabilities into daily migration work where organizational policies permit. Treat AI-generated SQL as unverified code and independently inspect, execute, test, and reconcile all AI-assisted conversions before they are accepted for migration use
Skills
- Fully Remote, Candidates must be based in North Dakota (ND), Utah (UT), Washington (WA), or Oregon (OR)
- Video participation is expected during interviews and appropriate working sessions
- Strong SQL query development and troubleshooting experience is required, with particular emphasis on existing Microsoft SQL Server workloads
- Demonstrated Snowflake platform expertise is also required, including the ability to support migration execution, validation, troubleshooting, and performance tuning
- Previous hands-on data warehouse migration experience is important because this consultant is being brought in specifically to accelerate the final migration phase and reduce delivery risk
- The successful candidate must be comfortable moving between query conversion, troubleshooting, data reconciliation, ETL support, Snowflake optimization, and collaboration with other members of the data team as priorities change
- The ability to independently diagnose problems and drive them through resolution is more important than experience limited to one narrow component of a migration
- ETL troubleshooting and optimization experience is highly desirable because the consultant may need to diagnose pipeline reliability and migration dependencies
- Power BI familiarity is beneficial for validating downstream reporting outputs and dashboard behavior, while Azure Data Factory experience would strengthen the consultant's ability to address pipeline-related migration dependencies
- Healthcare data environment experience is also desirable
Benefits
- Fully Remote work arrangement for candidates based in North Dakota, Utah, Washington, or Oregon.
- Gain hands-on exposure to the complex final stages of an enterprise data warehouse migration.
- Opportunity to work across SQL conversion, data validation, ETL troubleshooting, performance optimization, reporting validation, and production migration issues.
- Knowledge transfer to internal team members.
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