Remote Data Engineer - Teradata Migration

You will lead the hands-on migration of legacy Teradata workloads onto our modern cloud data platform. This is a delivery role, not an advisory one. You will be reading legacy SQL and stored procedures, understanding what they actually do, and rebuilding that logic in the new stack with tests and documentation behind it.

What you’ll do

1. Inventory and profile existing Teradata objects: tables, views, macros, BTEQ scripts, stored procedures, and the jobs that run them.

2. Translate Teradata SQL to the target platform dialect, including proprietary constructs (QUALIFY, MERGE behavior, SET vs MULTISET tables, PI/PPI-driven logic, RANK/CSUM and other Teradata-specific functions).

3. Rebuild transformation logic as modular, testable models in our transformation framework rather than lifting-and-shifting procedural code.

4. Design the target physical layout: clustering, partitioning, and load patterns appropriate to the new platform instead of carrying over Teradata indexing assumptions.

5. Build and run reconciliation between legacy and migrated outputs: row counts, aggregate checks, column-level comparison, and edge-case validation on a defined sample.

6. Document lineage, business logic uncovered during migration, and any deliberate behavior changes so downstream consumers can review them.

7. Coordinate cutover with downstream BI, Finance, Sales, and partner engineering consumers, including parallel-run windows and rollback plans.

8. Retire legacy objects once validated, and confirm no orphaned dependencies remain.

We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment regardless of their race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or recruiting process, please send a request to HR@insightglobal.com.To learn more about how we collect, keep, and process your private information, please review Insight Global’s Workforce Privacy Policy: https://insightglobal.com/workforce-privacy-policy/.

1. 5+ years in data engineering with substantial hands-on Teradata experience, including reading and reverse-engineering undocumented legacy SQL.

2. At least one completed Teradata-to-cloud migration where you owned significant scope, not just a supporting role.

3. Strong SQL, including window functions, complex joins, and performance tuning on large tables.

4. Working knowledge of a cloud data warehouse (Snowflake preferred; BigQuery, Redshift, or Databricks acceptable) and how its cost and performance model differs from Teradata.

5. Experience with a SQL transformation framework such as dbt, including testing and version control practices.

6. Python for orchestration, tooling, and one-off data validation work.

7. Git-based workflow: branching, pull requests, code review.

8. Demonstrated rigor around data validation. You should be able to describe how you proved a migration was correct, not just that it ran. 1. Prior work in retail, CPG, or ecommerce data, especially product catalog, GTIN/UPC, or item master domains.

9. Experience with Airflow or a comparable orchestrator.

10. Familiarity with migration accelerator tooling and its limits.

11. Comfort writing documentation for a mixed technical and business audience.

12. What success looks like in the first 90 days

13. A complete, prioritized inventory of in-scope Teradata objects with dependency mapping.

14. First migration wave delivered, validated, and running in parallel.

15. A repeatable reconciliation approach the rest of the team can reuse for later waves.