Data Engineer Lead

Job Description

Responsibilities Lead and mentor a team of data engineers, providing technical guidance, setting best practices, and overseeing task execution for the migration project. Design, develop, and architect scalable ETL processes to extract, transform, and load petabytes of data from on-premises SQL Server to GCP Cloud SQL PostgreSQL. Oversee the comprehensive analysis of existing SQL Server schemas, data types, stored procedures, and complex data models, defining strategies for their optimal conversion and refactoring for PostgreSQL. Establish and enforce rigorous data validation, quality, and integrity frameworks throughout the migration lifecycle, ensuring accuracy and consistency. Collaborate strategically with Database Administrators, application architects, business stakeholders, and security teams to define migration scope, requirements, and cutover plans. Lead the development and maintenance of advanced scripts (primarily Python) for automating large-scale migration tasks, complex data transformations, and reconciliation processes. Proactively identify, troubleshoot, and lead the resolution of complex data discrepancies, performance bottlenecks, and technical challenges during migration. Define and maintain comprehensive documentation standards for migration strategies, data mapping, transformation rules, and post-migration validation procedures. Ensure data governance, security, and compliance standards are meticulously applied throughout the migration process, including data encryption and access controls within GCP. Implement Schema conversion or custom schema mapping strategy for SQL Server to PostgreSQL shift Refactor and translate complex stored procedures and T-SQL logic to PostgreSQL-compatible constructs while preserving functional equivalence. Develop and execute comprehensive data reconciliation strategies to ensure consistency and parity between legacy and migrated datasets post-cutover. Design fallback procedures and lead post-migration verification and support to ensure business continuity. Ensuring metadata cataloging and data lineage tracking using GCP-native or integrated tools. Must-Have Skills Expertise in data engineering, specifically for Google Cloud Platform (GCP). Deep understanding of relational database architecture, advanced schema design, data modeling, and performance tuning. Expert-level SQL proficiency, with extensive hands-on experience in both T-SQL (SQL Server) and PostgreSQL. Hands-on experience with data migration processes, including moving datasets from on-premises databases to cloud storage solutions. Proficiency in designing, implementing, and optimizing complex ETL/ELT pipelines for high-volume data movement, leveraging tools and custom scripting. Strong knowledge of GCP services: Cloud SQL, Dataflow, Pub/Sub, Cloud Storage, Dataproc, Cloud Composer, Cloud Functions, and Bigquery. Solid understanding of data governance, security, and compliance practices in the cloud, including the management of sensitive data during migration. Strong programming skills in Python or Java for building data pipelines and automating processes. Experience with real-time data processing using Pub/Sub, Dataflow, or similar GCP services. Experience with CI/CD practices and tools like Jenkins, GitLab, or Cloud Build for automating the data engineering pipeline. Knowledge of data modeling and best practices for structuring cloud data storage for optimal query performance and analytics in GCP. Familiarity with observability and monitoring tools in GCP (e.g., Stackdriver, Prometheus) for real-time data pipeline visibility and alerting. Good-to-Have Skills Direct experience with GCP Database Migration Service, Storage Transfer Service, or similar cloud-native migration tools. Familiarity with data orchestration using tools like Cloud Composer (based on Apache Airflow) for managing workflows. Experience with containerization tools like Docker and Kubernetes for deploying data pipelines in a scalable manner. Exposure to DataOps tools and methodologies for managing data workflows. Experience with machine learning platforms like AI Platform in GCP to integrate with data pipelines. Familiarity with data lake architecture and the integration of BigQuery with Google Cloud Storage or Dataproc.