Note: (GC, GC-EAD, OPT, CPT on C2C will not be workable)
Core Responsibilities
- Design, implement, and optimize clean, well-structured, and performant analytical datasets to support high-volume reporting, business analysis, and data science model development.
- Architect, build, and maintain scalable and robust data pipelines for diverse applications, including business intelligence and advanced analytics.
- Implement and support Big Data solutions for both batch (scheduled) and real-time/streaming analytics.
- Work closely with product managers and business teams to understand data requirements and translate them into technical solutions.
- Collaborate with DevOps teams to ensure smooth deployment, monitoring, and maintenance of data pipelines and infrastructure in cloud environments.
Required Skills & Experience
- Cloud Platform Expertise (GCP Focus): Extensive hands-on experience working in dynamic cloud environments, with a strong preference for Google Cloud Platform (GCP) services, specifically:
- BigQuery: Expert-level skills in data ingestion, performance optimization, and data modeling within a petabyte-scale environment.
- Experience with other relevant GCP services like Cloud Storage, Cloud Dataflow/Beam, or Pub/Sub.
- Python: Expert-level programming proficiency in Python, including experience with relevant data engineering libraries.
- SQL: A solid command of advanced SQL for complex querying, data processing, and performance tuning.
- Data Pipeline Orchestration: Prior experience using workflow management and orchestration tools (e.g., Apache Airflow, Cloud Composer, Dagster, or similar).
- DevOps/CI/CD:
- Strong understanding of DevOps principles and practices.
- Experience with CI/CD pipelines, automation tools, and deployment strategies.
- Familiarity with version control systems (Git) and tools like GitLab CI/CD, GitHub Actions, or Jenkins.
- Knowledge of containerization (Docker) and orchestration tools (Kubernetes) is a plus.
- Monitoring & Automation: Ability to implement monitoring solutions and automate operational tasks to ensure reliability and scalability.