Senior Data Engineer & Analytics Developer - Remote - Banking - W2 - JOBID729

Core Competencies Data Engineering and Pipeline Development • Deep, hands-on experience with Google BigQuery — including dataset design, partitioning/clustering strategies, materialized views, and cost-optimization techniques. • Proficiency in Cloud Composer (Apache Airflow) for orchestrating complex, production-grade data pipelines with proper scheduling, retry logic, and dependency management. • Experience building and maintaining Vertex AI Pipelines for ML workflows and data transformation at scale. • Advanced SQL skills — able to write complex, performant, and maintainable queries across large datasets including window functions, CTEs, recursive queries, and query optimization. • Strong Python proficiency — comfortable building data transformation scripts, pipeline logic, custom Airflow operators, API integrations, and automation tooling. Data Architecture and Scalable Design • Proven ability to design layered data architectures using patterns such as Medallion (bronze/silver/gold), Dimensional Modeling (star schema), Data Vault, and targeted denormalization — and knows when to apply each based on the use case. • Track record of building modular, multi-purpose datasets rather than project-specific tables — thinks in terms of canonical models and shared dimensions. • Understands when to create new tables versus when to extend, view, or restructure existing assets to avoid unnecessary duplication and table sprawl. • Applies best practices around naming conventions, schema organization, documentation, and lifecycle management so that the architecture remains navigable as it scales. Tableau Dashboard Development • Hands-on experience building production-quality Tableau dashboards — from data source configuration and extract optimization to interactive visual design. • Ability to translate business questions into clear, intuitive visualizations that non-technical stakeholders can self-serve from. • Familiarity with Tableau performance tuning, published data sources, and server/cloud publishing workflows. • Understands the relationship between upstream data modeling decisions and downstream dashboard performance — designs the data layer with the visualization in mind. Technical Stack • Cloud Platform: Google Cloud Platform (GCP) • Data Warehouse: BigQuery (advanced) • Orchestration: Cloud Composer / Apache Airflow • ML Pipelines: Vertex AI Pipelines • Visualization: Tableau (Desktop, Server/Cloud) • Languages: Python (advanced), SQL (advanced) • Infrastructure: Terraform (preferred), GCS, Cloud Functions • Version Control: Git / GitLab Experience and Qualification • s5+ years in a data engineering role, with meaningful GCP/BigQuery experience • .Advanced proficiency in Python and SQL as daily working languages • .Demonstrated experience designing and maintaining shared, reusable data models in an enterprise or multi-team environment • .Familiarity with data architecture patterns including Medallion, star schema, and Data Vault • .Portfolio or examples of Tableau dashboards built on well-structured data layers • .Familiarity with CI/CD practices for data pipelines and infrastructure-as-code concepts • .Strong communicator who can work with cross-functional teams to gather requirements and translate them into scalable data solutions .