Sr Data Engineer & Analytics Developer

Senior Data Engineer & Analytics Developer Contract | GCP • BigQuery • Tableau • Python Employment Type: Contract / contract-to-hire Pay Rate: $65.00/hr Role Summary We are seeking a Data Engineer with strong analytics capabilities who can own the full data lifecycle — from scalable pipeline development to polished Tableau dashboards. The ideal candidate thinks architecturally, designs datasets for reuse and longevity, and brings a builder’s mindset grounded in efficiency, modularity, and long-term sustainability of the data platform. Advanced proficiency in Python, SQL, and Tableau is required. Key Responsibilities Data Engineering & Pipeline Development • Design, build, and maintain production-grade data pipelines using Google BigQuery — including dataset design, partitioning/clustering strategies, materialized views, and cost optimization. • Orchestrate complex pipelines using Cloud Composer (Apache Airflow) with proper scheduling, retry logic, and dependency management. • Build and maintain Vertex AI Pipelines for ML workflows and large-scale data transformation. • Write advanced, performant SQL across large datasets including window functions, CTEs, recursive queries, and query optimization. • Develop Python scripts for data transformation, pipeline logic, custom Airflow operators, API integrations, and automation tooling. Data Architecture & Scalable Design • Design layered data architectures using patterns such as Medallion (bronze/silver/gold), Dimensional Modeling (star schema), Data Vault, and targeted denormalization — applying the right pattern for each use case. • Build modular, multi-purpose datasets rather than project-specific tables; think in terms of canonical models and shared dimensions. • Determine when to create new tables versus extending, viewing, or restructuring existing assets to prevent unnecessary duplication and table sprawl. • Apply best practices around naming conventions, schema organization, documentation, and lifecycle management. Tableau Dashboard Development • Build production-quality Tableau dashboards — from data source configuration and extract optimization to interactive visual design. • Translate business questions into clear, intuitive visualizations that non-technical stakeholders can self-serve. • Tune Tableau performance; manage published data sources and server/cloud publishing workflows. • Design the data layer with downstream visualization performance in mind. Requirements • 5+ 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 able to work cross-functionally to gather requirements and deliver scalable data solutions. Preferred Qualifications • Experience with Terraform for infrastructure-as-code. • Familiarity with Google Cloud Storage (GCS) and Cloud Functions. • Version control experience with Git/GitLab in a data engineering context. 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 What Sets The Ideal Candidate Apart • Architecture-first thinking — asks “Does this already exist? Can I extend what’s here? Will this serve more than just today’s ask?” before writing a single line of code. • Efficiency over volume — measures success by how few tables and pipelines are needed to support a growing number of use cases, not how many are created. • End-to-end ownership — comfortable moving from raw ingestion through to a polished Tableau dashboard, understanding how each layer impacts the next. • Pragmatic scalability — designs for the future without over-engineering for the present; builds foundations that absorb new projects without architectural rework.