Senior Data Engineer & Analytics Developer
Contract | GCP
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.
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