Data Engineer - Journeyman

Data Warehousing & Analytics (Core Requirement): Advanced proficiency in SQL with extensive experience in cloud data warehousing. Must have deep knowledge of data modeling, schema design, and query optimization. ○ GCP Preference: Significant, hands-on experience with Google BigQuery (partitioning, clustering, BQ scripting, cost optimization) is essential. Modern Data Pipeline Expertise (Must-Have Foundation): Demonstrable expertise in building, deploying, and scaling complex data pipelines using at least one of the following foundational technologies: ○ Orchestration: Deep expertise in Apache Airflow (designing, deploying, scaling, and managing complex DAGs). GCP Preference: Hands-on experience with Google Cloud Composer. ○ Processing: Proven ability to build and optimize robust, high-throughput batch and streaming data pipelines using Apache Beam. GCP Preference: Direct experience managing Beam pipelines at scale using Google Cloud Dataflow. ○ (Note: This role offers the flexibility to architect solutions and select the appropriate services to meet project requirements). Relational Database Expertise (Must-Have Foundation): Solid foundation and practical experience with RDBMS architecture, management, and optimization, specifically with PostgreSQL. ○ GCP Preference: Familiarity with managed database services, particularly Google Cloud SQL (for PostgreSQL or MySQL), is a significant advantage. Core Programming & Infrastructure: Fluency in Python (preferred) or Java for data pipeline development. Strong understanding of CI/CD, Git, and Infrastructure-as-Code (e.g., Terraform). Cloud Architecture & GCP (Highly Desirable): Broad experience in architecting, building, and managing solutions on a major cloud platform, with a strong preference for Google Cloud Platform (GCP) beyond just its data services. Containerization & Kubernetes (Highly Desirable): Understanding of container concepts (Docker) and practical experience with Kubernetes, particularly Google Kubernetes Engine (GKE). ML Engineering Exposure (Plus): Experience supporting machine learning workflows, including data preparation, feature engineering, and operationalizing data pipelines for ML models. ○ GCP Preference: Any exposure to Google Cloud Vertex AI (Pipelines, Feature Store, Training) is a major plus. Bachelor's or master's degree in computer science, Data Science, or a related field. 5-8 years of experience of hands-on experience in data engineering, demonstrating a clear progression in designing, building, and maintaining scalable data-intensive systems. Demonstrated ability to communicate complex data issues to both technical and non-technical stakeholders.