ETL Modernization Architect - 1646
Onsite/Hybrid/Remote: Remote Duration: 12 months Rate Range: $75 in W2 Work Authorization: GC and US Citizens Only Must Have: ETL/ELT architecture and modernization IBM DataStage Databricks and Apache Spark Delta Lake, Unity Catalog, and Photon AWS Glue, Redshift, and Lambda Python and Unix/Linux scripting Terraform and CI/CD Large-scale database and data warehouse migrations Data governance, quality, and observability Responsibilities: Define the architecture and migration strategy for modernizing legacy ETL and ELT pipelines. Assess IBM DataStage jobs, databases, and data warehouses for migration readiness. Design scalable data solutions using Databricks Spark on AWS. Establish architecture standards, reusable frameworks, and governance controls. Design CI/CD pipelines for automated builds, testing, and deployment. Lead technical design reviews, migration planning, and artifact validation. Define parity, functional, UAT, regression, and performance testing strategies. Ensure schema validation, data quality, lineage, security, and production readiness. Guide cutover, go-live, hypercare, and operational stabilization activities. Oversee the decommissioning of legacy DataStage jobs and related components. Create operational documentation and conduct knowledge-transfer sessions. Provide technical direction to developers and engineering teams. Qualifications: Extensive experience designing enterprise ETL/ELT architectures. Hands-on experience with IBM DataStage and Databricks modernization projects. Strong experience with Databricks, Delta Lake, Unity Catalog, Photon, and Spark. Strong knowledge of AWS data services, including Glue, Redshift, and Lambda. Experience designing large-scale database and data warehouse migration programs. Proficiency in Python and Unix/Linux scripting. Experience with Terraform and automated deployment pipelines. Knowledge of data governance, observability, lineage, and operational readiness. Experience defining testing and production-readiness standards. Experience working in Agile/Scrum environments and PI planning. Nice to Have: GitLab or Azure DevOps experience JIRA experience Automated data-testing framework experience Experience leading enterprise cutovers and hypercare activities Experience mentoring data engineering teams
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