Senior Data Migration Engineer

Stellenbeschreibung:

Plan, coordinate, and oversee enterprise data migration, conversion, and archival efforts
Develop migration strategies, schedules, dependencies, and execution procedures
Build and maintain migration scripts, SQL, and ETL/ELT pipelines for legacy-to-cloud data transfers
Execute extraction, transformation, loading, and post-load validation activities
Perform data profiling, cleansing, mapping, and transformation
Validate migrated data through reconciliation, completeness checks, and QA testing
Manage cutover activities, including pre-checks, execution, validation, and sign-off
Automate data reconciliation and validation processes
Support migration testing, mock runs, rehearsals, cutovers, and rollback procedures
Design and implement data archival processes aligned with retention and governance standards
Troubleshoot migration issues and discrepancies
Document migration procedures, mappings, runbooks, logs, issue registers, and execution reports
Collaborate with governance, quality, business, DBA, platform engineering, architecture, application, and data teams
Tune migration jobs and optimize performance for large-scale, high-volume datasets
Ensure compliance with security, privacy, classification, retention, and governance requirements
Support production cutovers and post-migration stabilization
Manage and optimize cloud data platforms, including account setup, configuration, scaling, performance tuning, and warehouse/lakehouse operations
Govern databases, schemas, roles, and RBAC according to security, IAM, and compliance policies
Monitor and troubleshoot pipelines, queries, concurrency, and resource utilization
Implement cost optimization and support financial reporting, forecasting, and TCO analysis
Automate and document provisioning, access management, data loading, and monitoring using Terraform, dbt, SQL, Python, or similar tools
Partner with data engineers, BI teams, and architects to design data models and optimize ELT workflows
Support security audits, ATO artifacts, incident response, disaster recovery/high availability testing, and operational runbooks

Requirements

5+ years of related experience
MA/MS degree with 5+ years or BS/BA degree with 7+ years of general experience in information systems and 5+ years of specialized experience
Strong data engineering and architecture background with deep ETL/ELT and migration experience
Experience with Informatica, Talend, AWS DMS, and ADF
Proficiency in Python, Bash, SQL, and migration automation
Expertise in AWS data migration services, cloud-native data platforms, and hybrid cloud environments
Preferred hands-on experience with Snowflake, Databricks, Redshift, SQL Server, Oracle, PostgreSQL, and large-scale analytical workloads
Strong SQL performance tuning, query optimization, indexing, partitioning, and execution-plan analysis
Proficiency with Terraform, dbt, IaC, and automation frameworks
Experience with CI/CD, GitOps, DevSecOps, and enterprise build/release pipelines
Experience troubleshooting complex migration issues under tight cutover windows
Experience with containerized application workflows and cloud platform operations
Familiarity with FedRAMP, NIST, and JISF frameworks
Experience with log/metric analysis and cloud performance tuning
Preferred experience supporting Federal Government programs or regulated environments
Experience with FinOps, cost modeling, and cloud optimization
Preferred experience optimizing large-scale analytical workloads
Preferred certifications including CDMP, Snowflake SnowPro Advanced, Databricks Certified Data Professional, AWS certifications, Docker Certified Associate, Certified Kubernetes Administrator, DevSecOps, ITIL, AWS SysOps, or Google Cloud DevOps Engineer
No US citizenship required
Clearance not required

Core Competencies

Demonstrates expertise in data migration strategies, ETL/ELT processes, and cloud data platform management, with a strong focus on automation, performance tuning, and compliance with governance standards. Proficient in troubleshooting complex migration issues and optimizing large-scale analytical workloads.