Technical Manager with extensive experience in GCP and data engineering for cloud modernization projects. Leading cross-functional teams in optimizing data warehouses and managing technical programs.
Responsibilities
Design, develop, and optimize data warehouses, ensuring high performance and cost efficiency.
Lead cross-functional programs focused on cloud modernization, legacy migration, and data transformation initiatives on GCP.
Drive execution across multiple technical teams including data engineering, application development, cloud ops, and architecture.
Translate business goals into structured technical programs with clear milestones, success metrics, and risk mitigation strategies.
Serve as the technical voice in executive updates, steering committees, and cross-org syncs.
Work closely with engineering teams to design scalable and secure cloud-native solutions.
Implement program governance, best practices, and agile delivery methods across initiatives.
Collaborate with client stakeholders to ensure successful deployment and adoption of modernization solutions.
Requirements
13+ years of experience working as a GCP Data Engineer or similar role.
Strong expertise in Google BigQuery – performance tuning, partitioning, clustering, and cost optimization.
Hands-on experience with ETL tools and frameworks, including Cloud Dataflow (Apache Beam), Cloud Dataproc (Spark), and Cloud Composer (Airflow).
Proficiency in SQL, Python, and Shell scripting for data transformation and automation.
Experience with Google Cloud Storage, Pub/Sub, and Cloud Functions.
Strong understanding of GCP IAM, security best practices, and data governance.
Familiarity with Terraform, Cloud Build, and CI/CD for infrastructure automation.
Ability to work with large-scale datasets and real-time streaming data processing.
Experience in data modeling, schema design, and optimization techniques.
Strong analytical and problem-solving skills.
GCP Professional Data Engineer Certification is a plus.
Experience with Machine Learning workflows on GCP (Vertex AI, AutoML, TensorFlow on AI Platform) is a plus.
Experience with Kubernetes (GKE) and containerized deployments is a plus.
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