Lead GCP Analytics Engineer

GC Holder or US Citizens need Local and LinkediN profile Key Skills:     GCP, SQL, Python, financial payment processing project preferred We’re seeking a Lead Analytics Engineer to help design, model, and scale a modern data environment for a global payment processing organization. The company manages large volumes of data across multiple business units, and this role will play a key part in organizing and maturing that landscape as part of a multi-year strategic roadmap. This position is ideal for a lead-level analytics engineer who can architect data solutions, build robust models, and stay hands-on with development. Qualifications • 10+ years of experience in an Analytics Engineering role. • Lead-level resource to help with design and architecture • Expert in SQL and dbt with demonstrated modeling experience (Vault, 3NF, Dimensional). • Hands-on experience with BigQuery or other cloud data warehouses. • Proficiency in Python and Docker. • Experience with Airflow (Composer), Git, and CI/CD pipelines. • Strong attention to detail and communication skills; able to interact with both technical and business stakeholders. • Experience in financial services or payments is a plus but not required. • Bachelor’s degree in Economics, Mathematics, Computer Science, or related field. Role Focus • Architect and build new GCP data models using dbt and modern modeling techniques. • Partner closely with leadership and business teams to translate complex requirements into technical solutions. • Support initiatives focused on Finance and Payments data domains. • Drive structure and clarity within a growing analytics ecosystem. Technical Environment • Primary Data Warehouse: Google BigQuery (mandatory) • Nice to Have: Snowflake, Redshift • Orchestration: Airflow (GCP Composer) • Languages: Expert-level SQL / dbt; strong Python required • Other Tools: GCP or AWS, Fivetran, Apache Beam, Looker or Preset, Docker • Modeling Techniques: Vault 2.0, 3NF, Dimensional Modeling, etc • Version Control: Git / CI-CD • Quality Tools: dbt-Elementary, dbt-Osmosis, or Great Expectations preferred Responsibilities • Business Stakeholder Engagement • Gather and document complex business requirements. • Translate business needs into scalable, maintainable data products. • Serve as a trusted data partner across multiple departments. • Data Modeling & Transformation • Design and implement robust, reusable data models within the warehouse. • Develop and maintain SQL transformations in dbt. • Optimize existing models and queries for performance, cost-efficiency, and maintainability. • Data Pipeline & Orchestration Build and maintain reliable data pipelines in collaboration with data engineering. • Utilize orchestration tools (Airflow) to manage and monitor workflows. • Manage and support dbt environments and transformations. • Data Quality & Governance • Implement validation checks and quality controls to ensure data integrity. • Define and enforce data governance best practices, including lineage and access control. • Enable Data Democratization & Self-Service Analytics • Curate and prepare datasets for analysts, business users, and data scientists. • Develop semantic layers for consistent and accessible reporting.