Data Migration / Conversion Specialist Recruiters

What this hire really comes down to

Before moving records, agree which relationships and business meanings must survive, because a successful load is not enough to prove a migration worked. You need someone who views data as a physical asset that must be packed, moved, and reassembled without breakage. These projects often suffer because the migration logic is treated as a secondary task by software engineers. When you hire for this, look for a technician who treats data source mapping and schema conversion as their primary professional identity rather than a transitionary duty.

This specialist must have an intimate understanding of data loss risks. They need to define the logic for data transformation, validate the post-migration state, and handle the discrepancy logs that inevitably arise. Do not look for someone who relies solely on automated migration tools provided by cloud vendors.

Expect them to be obsessive about data lifecycle documentation. If they cannot explain how they plan to reconcile source and target counts across different environments, they are not ready for a production cutover. They should lead the discussion on rollback strategies, focusing specifically on how long it takes to revert to the old system if the migration fails during the production window.

What we check before you see anyone

• Schema Mapping Validation

  Ask them to explain their process for handling source-to-target schema mismatches. A strong candidate provides a concrete example of how they remapped data types that did not exist in the target environment. If they talk only about using basic ETL tools without explaining the manual adjustment logic, they will likely fail when encountering complex custom objects.

• Data Integrity Reconciliation

  The candidate must describe a repeatable method for ensuring row counts and checksums match between source and destination. They should provide evidence of using SQL scripts to perform spot checks on millions of records. Avoid candidates who suggest running a final count check only after the entire migration is complete.

• Orchestration Logic

  They need to demonstrate how they manage the order of operations for dependent tables. A competent specialist use tools like Airflow or Dagster to ensure parent records move before child records. If they describe a manual, spreadsheet-based tracking approach, they lack the technical rigor required for high-volume database migrations.

• Environment Parity Testing

  Check their ability to script out environment testing. They should have experience performing dry runs in a staging environment that mirrors production data volume. Ask them how they handled performance bottlenecks during a previous trial and what changes they implemented to accelerate the final execution window.

Where we find them

Target candidates currently operating in high-volume, legacy-to-cloud transition environments. Look for individuals in financial services or healthcare sectors where audit trails and data accuracy are strictly enforced. These environments force engineers to develop a disciplined approach to data movement because the cost of error is significantly higher than in general software development.

Seek out those who have moved between major platforms like moving from on-premise Oracle or SQL Server clusters to BigQuery or Snowflake. Motivation is key here. The right person enjoys the forensic challenge of cleaning messy data as much as they enjoy the architecture side. If they are solely focused on cloud migration as a way to get out of legacy maintenance, they might ignore the technical rigor needed to protect your data during the move.

Mistakes we see

• Hiring based purely on cloud certification without verifying their experience in manual data validation or SQL-heavy cleanup.

• Failing to ask how they handle historical data that no longer meets modern schema requirements, which usually leads to massive data bloat in the target system.

• Overlooking their lack of experience with downtime estimation, leading to a migration window that extends well beyond the business capacity.

The search in three steps

1. Agree the brief

   Start by defining your strict cutover windows and the tolerance for downtime. During the intake call, identify if this project is a lift-and-shift operation or a complete schema redesign as that changes the technical profile entirely.

2. Find and speak with people

   Screen for experience with large-scale dry runs. A headhunt should focus on finding candidates who have completed at least two full-scale data migrations in the last three years to ensure they have survived the stress of a production move.

3. Introduce the people who fit

   Evaluate candidates based on their ability to explain failed migrations. Ask about the single biggest data corruption event they encountered and how they successfully corrected the source-to-target mapping error before it impacted end users.

Questions about this search

Where we fill this role

• SaaS and B2B Software
• Fintech, Payments and Insurtech
• Banking, Capital Markets and Trading
• Healthtech and Digital Health
• Health Systems, Payers and Provider IT
• AI-Native Companies

Related roles and stacks

• Data Engineering, Analytics and BI
• Data Engineer
• Analytics Engineer
• Data Architect
• BI Developer
• Database Administrator
• Chief Data Officer / Head of Data
• Python
• GCP
• Snowflake
• Databricks
• Oracle

Hiring guidance

Plan this hire

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