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Position: SQL Server to Snowflake Migration Consultant
Company: Confidential
Location: Remote
Employment Type: Full-Time Consulting Engagement
Work Arrangement: Fully Remote, Candidates must be based in North Dakota (ND), Utah (UT), Washington (WA), or Oregon (OR).
Interview: Video participation is expected during interviews and appropriate working sessions.
Overview
The SQL Server to Snowflake Migration Consultant will provide hands-on technical expertise to accelerate critical migration activities associated with the organization's transition from Microsoft SQL Server to Snowflake. The primary focus of the assignment is converting existing SQL Server queries and workloads to Snowflake-compatible SQL, troubleshooting migration issues, validating data and results, and reducing technical and delivery risk during the final phase of the migration.
This is an execution-focused position rather than an advisory-only Snowflake role. The consultant must be able to personally analyze existing Microsoft SQL queries, understand their underlying business and data logic, convert that logic appropriately for Snowflake, troubleshoot differences between the environments, and validate that converted workloads produce the expected results.
The consultant will also support ETL pipelines, Snowflake performance tuning, reporting and dashboard validation, and knowledge transfer to internal team members. As migration priorities change, the individual should be comfortable moving between query conversion, troubleshooting, data reconciliation, pipeline support, reporting validation, and other assignments required by the broader data team.
Purpose: Play a direct role in completing a critical data-platform migration by helping move existing SQL Server workloads successfully into Snowflake. The consultant's work will reduce migration risk by ensuring that converted queries, data, pipelines, and reporting outputs operate correctly as the organization completes the final migration phase.
Growth: Gain hands-on exposure to the complex final stages of an enterprise data warehouse migration rather than working only on isolated Snowflake development tasks. The consultant will work across SQL conversion, data validation, ETL troubleshooting, performance optimization, reporting validation, and production migration issues while collaborating with the broader data organization.
This opportunity is best suited to a hands-on data professional who enjoys solving difficult migration problems and seeing measurable progress from the work. The role provides the opportunity to take existing SQL Server workloads, determine how they should operate in Snowflake, resolve conversion and performance issues, validate the resulting data, and help the team move critical migration activities toward successful completion.
Objectives
1. Convert Microsoft SQL Server Workloads to Snowflake. 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. Success will be measured by the volume and complexity of workloads successfully converted, conversion accuracy, successful validation, and reduction of the outstanding migration backlog. 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.
2. Troubleshoot and Resolve Migration Issues During the Final Migration Phase. 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. Success will be measured by timely resolution of migration blockers, reduction in unresolved defects, and the consultant's ability to independently move assigned migration problems toward closure.
3. Validate and Reconcile Migrated Data and Reporting Outputs. 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. Success will be measured by completed reconciliation, resolution of material discrepancies, validated reporting outputs, and increased confidence that migrated workloads are ready for use.
4. Improve Snowflake Workload Performance and Support Migration Stability. 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. Success will be measured by improved workload performance, reliable pipeline execution, resolution of priority performance issues, and reduced migration-related operational risk.
Subtasks
1. Assess the Migration Backlog and Priority SQL Workloads. 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. Success will be measured by rapid understanding of the assigned migration environment and clear progress against the highest-priority migration activities.
2. Perform Hands-On SQL Server-to-Snowflake Query Conversion. 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. Success will be measured by successfully converted and tested workloads, quality of the resulting SQL, and continued reduction of the migration backlog.
3. Reconcile Data and Investigate Conversion Differences. 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. Success will be measured by accurate reconciliation, timely defect resolution, and demonstrated confidence in migrated data.
4. Troubleshoot ETL Pipelines and Migration Dependencies. 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. Success will be measured by timely restoration of pipeline functionality, reduced recurring failures, and continued movement of dependent workloads through the migration process.
5. Tune Snowflake Queries and Validate Production Readiness. 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. Success will be measured by acceptable workload performance, accurate results, and resolution of significant performance-related migration risks.
6. Validate Reporting and Transfer Migration Knowledge. 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. Success will be measured by validated reporting behavior, reduced downstream migration defects, and the internal team's ability to support converted workloads after knowledge transfer.
7. Continuously Evaluate and Integrate AI to Improve Performance. 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. Success will be measured by demonstrable improvements in migration productivity or quality without compromising data accuracy, security, maintainability, or technical judgment.
Capabilities
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.
Preferred Experience – Bonus Points
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. This background may help the consultant understand the importance of data quality, compliance awareness, and reliable operational reporting, but it should not substitute for the core requirement of demonstrated SQL Server-to-Snowflake migration capability.
Definition of Success
By the conclusion of the engagement, the consultant has materially accelerated the final SQL Server-to-Snowflake migration by converting priority workloads, resolving migration blockers, validating and reconciling data, improving Snowflake performance where necessary, supporting reliable ETL execution, and confirming that affected reporting continues to operate as expected.
The most important evidence of success will be working migration outcomes rather than advisory recommendations. The consultant will have taken actual Microsoft SQL Server workloads, successfully converted and troubleshot them for Snowflake, validated their outputs, and helped move them toward stable production use while leaving the internal data team better equipped to support the migrated environment.
Company: Confidential
Location: Remote
Employment Type: Full-Time Consulting Engagement
Work Arrangement: Fully Remote, Candidates must be based in North Dakota (ND), Utah (UT), Washington (WA), or Oregon (OR).
Interview: Video participation is expected during interviews and appropriate working sessions.
Overview
The SQL Server to Snowflake Migration Consultant will provide hands-on technical expertise to accelerate critical migration activities associated with the organization's transition from Microsoft SQL Server to Snowflake. The primary focus of the assignment is converting existing SQL Server queries and workloads to Snowflake-compatible SQL, troubleshooting migration issues, validating data and results, and reducing technical and delivery risk during the final phase of the migration.
This is an execution-focused position rather than an advisory-only Snowflake role. The consultant must be able to personally analyze existing Microsoft SQL queries, understand their underlying business and data logic, convert that logic appropriately for Snowflake, troubleshoot differences between the environments, and validate that converted workloads produce the expected results.
The consultant will also support ETL pipelines, Snowflake performance tuning, reporting and dashboard validation, and knowledge transfer to internal team members. As migration priorities change, the individual should be comfortable moving between query conversion, troubleshooting, data reconciliation, pipeline support, reporting validation, and other assignments required by the broader data team.
Purpose: Play a direct role in completing a critical data-platform migration by helping move existing SQL Server workloads successfully into Snowflake. The consultant's work will reduce migration risk by ensuring that converted queries, data, pipelines, and reporting outputs operate correctly as the organization completes the final migration phase.
Growth: Gain hands-on exposure to the complex final stages of an enterprise data warehouse migration rather than working only on isolated Snowflake development tasks. The consultant will work across SQL conversion, data validation, ETL troubleshooting, performance optimization, reporting validation, and production migration issues while collaborating with the broader data organization.
This opportunity is best suited to a hands-on data professional who enjoys solving difficult migration problems and seeing measurable progress from the work. The role provides the opportunity to take existing SQL Server workloads, determine how they should operate in Snowflake, resolve conversion and performance issues, validate the resulting data, and help the team move critical migration activities toward successful completion.
Objectives
1. Convert Microsoft SQL Server Workloads to Snowflake. 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. Success will be measured by the volume and complexity of workloads successfully converted, conversion accuracy, successful validation, and reduction of the outstanding migration backlog. 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.
2. Troubleshoot and Resolve Migration Issues During the Final Migration Phase. 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. Success will be measured by timely resolution of migration blockers, reduction in unresolved defects, and the consultant's ability to independently move assigned migration problems toward closure.
3. Validate and Reconcile Migrated Data and Reporting Outputs. 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. Success will be measured by completed reconciliation, resolution of material discrepancies, validated reporting outputs, and increased confidence that migrated workloads are ready for use.
4. Improve Snowflake Workload Performance and Support Migration Stability. 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. Success will be measured by improved workload performance, reliable pipeline execution, resolution of priority performance issues, and reduced migration-related operational risk.
Subtasks
1. Assess the Migration Backlog and Priority SQL Workloads. 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. Success will be measured by rapid understanding of the assigned migration environment and clear progress against the highest-priority migration activities.
2. Perform Hands-On SQL Server-to-Snowflake Query Conversion. 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. Success will be measured by successfully converted and tested workloads, quality of the resulting SQL, and continued reduction of the migration backlog.
3. Reconcile Data and Investigate Conversion Differences. 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. Success will be measured by accurate reconciliation, timely defect resolution, and demonstrated confidence in migrated data.
4. Troubleshoot ETL Pipelines and Migration Dependencies. 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. Success will be measured by timely restoration of pipeline functionality, reduced recurring failures, and continued movement of dependent workloads through the migration process.
5. Tune Snowflake Queries and Validate Production Readiness. 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. Success will be measured by acceptable workload performance, accurate results, and resolution of significant performance-related migration risks.
6. Validate Reporting and Transfer Migration Knowledge. 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. Success will be measured by validated reporting behavior, reduced downstream migration defects, and the internal team's ability to support converted workloads after knowledge transfer.
7. Continuously Evaluate and Integrate AI to Improve Performance. 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. Success will be measured by demonstrable improvements in migration productivity or quality without compromising data accuracy, security, maintainability, or technical judgment.
Capabilities
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.
Preferred Experience – Bonus Points
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. This background may help the consultant understand the importance of data quality, compliance awareness, and reliable operational reporting, but it should not substitute for the core requirement of demonstrated SQL Server-to-Snowflake migration capability.
Definition of Success
By the conclusion of the engagement, the consultant has materially accelerated the final SQL Server-to-Snowflake migration by converting priority workloads, resolving migration blockers, validating and reconciling data, improving Snowflake performance where necessary, supporting reliable ETL execution, and confirming that affected reporting continues to operate as expected.
The most important evidence of success will be working migration outcomes rather than advisory recommendations. The consultant will have taken actual Microsoft SQL Server workloads, successfully converted and troubleshot them for Snowflake, validated their outputs, and helped move them toward stable production use while leaving the internal data team better equipped to support the migrated environment.