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This is a remote position.
As a Sr. Data Engineer, you will help us refactor and move the existing ETL/ELT and analytics pipelines into the new environment, migrate the underlying data, and validate that everything lands correctly.
This is hands-on migration work. You'll be working alongside our engineers on well-defined pipelines with clear acceptance criteria, and you'll pick up related migration requests — including dashboard and reporting migrations — as they come up.
What You’ll Do
Work with one of our Staff Data Engineers to understand the migration plan, sequence your work against it, and provide regular progress updates.
Refactor existing ETL/ELT code written in PySpark, DABs and dbt from the current Databricks environment to meet the patterns and standards of the new account.
Migrate data pipelines and their underlying datasets, including backfills, and validate parity between source and target.
Build and run reconciliation checks — row counts, schema conformance, freshness, completeness — to confirm migrated data matches the legacy environment.
Migrate dashboards and downstream reporting assets to point at the new datasets, coordinating with the analysts who own them.
Update orchestration, scheduling, alerting, and runbooks so migrated pipelines are supportable after cutover.
Troubleshoot pipeline failures and data discrepancies during and after cutover.
Document what was migrated, what changed, and anything the team needs to know to own it going forward.
Take on adjacent migration and data engineering requests as the program evolves.
6+ years of experience in Data Engineering, Analytics Engineering, or Software Engineering working with production data systems.
Strong expertise in SQL and Python for building scalable data pipelines and transformations.
Strong familiarity with AI tools like Claude Code / Devin for SW development process
Hands-on experience building ELT pipelines using modern cloud data platforms (Databricks experience required).
Building robust testing frameworks for Data Migration ( Completeness, Quality etc)
Deep experience with dbt, including model development, testing, documentation, and CI/CD integration
Experience designing and operating production-grade data pipelines with monitoring and observability.
Strong collaboration skills and ability to partner with engineering teams, analysts, and operational stakeholders.
Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, or related field, or equivalent practical experience.
Prior data or platform migration experience
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