Overview
Skills
Job Details
Job Title: Data Engineer Ab Initio to Google Cloud Platform Migration
Location: Dallas (onsite)
Employment Type: Contract (12+ months)
Collaborate with architects and business stakeholders to gather migration requirements from Ab Initio to Google Cloud Platform.
Re-engineer and optimize ETL pipelines built in Ab Initio to leverage Google Cloud Platform-native tools such as BigQuery, Dataflow, Pub/Sub, Cloud Composer, and Dataproc.
Develop, test, and deploy data ingestion, transformation, and integration workflows on Google Cloud Platform.
Ensure data quality, governance, lineage, and compliance throughout the migration process.
Optimize the performance and scalability of cloud-based data pipelines.
Partner with DevOps teams to implement CI/CD pipelines and automation for data workflows.
Provide technical expertise in Ab Initio to Google Cloud Platform mapping, workload analysis, and re-platforming strategies.
Support troubleshooting, validation, and performance tuning of migrated workloads.
Document migration steps, architecture, and best practices for ongoing knowledge sharing.
Required Skills & Experience
5 8+ years of experience in Data Engineering with expertise in ETL and data pipeline development.
Strong hands-on expertise with Ab Initio (plans, graphs, PDL, EME, etc.).
Proven track record of migrating data pipelines from Ab Initio to Google Cloud Platform or similar modernization initiatives.
Proficiency with Google Cloud Platform services: BigQuery, Dataflow, Pub/Sub, Dataproc, Cloud Composer (Airflow).
Strong programming skills in SQL, Python, and Shell scripting.
Experience in data modeling, performance tuning, and large-scale data processing.
Good knowledge of data governance, security, and compliance in cloud environments.
Familiarity with CI/CD, Git, and DevOps practices for data engineering.
Nice to Have
Experience with other ETL migration tools/accelerators.
Knowledge of healthcare or retail domain data systems.
Exposure to Agile methodologies and collaborative delivery models.