Role Overview
We are seeking a Data Migration Engineer with 4–5 years of deep technical experience to drive complex data migration initiatives. In this role, you will design and build robust ETL pipelines within the GCP ecosystem, specifically handling large-scale data transitions into complex Salesforce schemas.

Technical Skills & Experience
Must-Have Requirements (4–5 Years Minimum)
GCP Ecosystem: Strong hands-on experience with GCP Dataproc and building ETL pipelines.
Apache Spark: Proficient in PySpark (preferred) for custom data transformations and business logic.
BigQuery: High proficiency in SQL, table design, partitioning, and performance optimization.
Python: Strong Python scripting skills for data manipulation.
Salesforce Integration: Solid understanding of the Salesforce data model and core Salesforce APIs (Bulk API, REST API).
Cloud Storage: Experience with GCS for file handling and cloud storage patterns.
Preferred (Nice-to-Have)
Salesforce Migrations: Prior experience with Salesforce data migration projects—specifically Health Cloud or Life Sciences Cloud.
CI/CD: Exposure to GitLab CI/CD (ability to understand pipeline YAML, not building it from scratch).
Data Security: Knowledge of Data Loss Prevention (DLP) implementation and PII handling best practices.
Compliance: Familiarity with healthcare data compliance standards (HIPAA, PIPEDA).

Technical Depth & Scale Expectations
Scale Benchmark: You will be expected to confidently manage large datasets containing 300GB+ of structured data and 3TB+ of documents, navigating complex environments with 70+ object schemas and intricate relationships.
To thrive in this role, you must possess the technical depth to:
Optimize & Debug: Troubleshoot complex Spark jobs and resolve intricate memory or performance bottlenecks.
Validate & Reconcile: Leverage advanced SQL skills for rigorous data validation and end-to-end reconciliation.

Data Migration Engineer

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