Must-Have Qualifications: • 10+ years in a Data Architect/Engineering role, with a strong focus on data modeling in Big Data environments (100TB+). • Advanced proficiency in Python and SQL for data mining, pipeline development, segmentation, and orchestration. • Deep experience with GCP, including BigQuery, GCS Buckets, IAM, Google Cloud Storage (GCS), Cloud Function, Composer, and VertexAI. • Proven ability to analyze and troubleshoot complex data pipelines through root-cause analysis. • Hands-on experience using DBT for data modeling and transformation workflows. • Strong understanding and application of ERD architecture in enterprise data systems. Plusses: • AI Agent, ADK, Mult-Agent, Gen AI Day-to-Day Responsibilities: Our leading entertainment client is expanding their data team and seeking a Senior Data Engineer with deep expertise in GCP, Big Data, Python, SQL, DBT, and ERD architecture. This cross-functional team operates across three core areas: AI/ML, Data Engineering, and Data Analytics. • Collaborate with stakeholders across subscription, viewership, marketing, paid media, CRM, and more. • Support data visualization initiatives and ensure data is accessible and actionable. • Work in a highly collaborative environment that values fast learners and team players. • Spend approximately 80% of your time in hands-on development using Python and SQL. Team Dynamic: Data Analytics Engineering (Approx. 80% of time) • Primary Focus: Analyze and maintain data pipelines, conduct root-cause analysis to resolve data-related issues. • Tools & Skills: • Proficient in Python and SQL for pipeline development. • Initially focused on learning and maintaining existing pipelines, with a gradual transition to building new ones. • Mindset: Business-oriented with a strong analytical approach. AI/ML Initiatives (Approx. 5% of time) • Objective: Support AI and machine learning efforts using modern platforms such as Vertex AI. • Scope: Limited involvement, primarily assisting with existing initiatives and tools. Data Engineering (Approx. 15% of time) • Responsibilities: Design and implement scalable data models, pipelines, and architecture. • Technical Stack: Heavy use of Python and SQL to ensure robust and efficient data infrastructure.