> Markdown version of [/jobs/ext/4667554-data-analyst-analytics-engineer](https://www.wearedevelopers.com/jobs/ext/4667554-data-analyst-analytics-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Data Analyst / Analytics Engineer

- **Company:** Blueprint School Network, Inc.
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Agile Methodology, Data Analysis, Business Logic, Systems Engineering, BigQuery, Cloud Database, Data Validation, Document-Oriented Databases, Reverse Engineering, SQL Databases, Tableau (Software), Sql Optimization, Google Data Studio, Snowflake, Data Layers, Core Data, Information Technology, Data Analytics, Google Bigquery, Code Restructuring, Looker Analytics
- **Published:** November 11, 2026
- **Apply:** https://fusemachines.applytojob.com/apply/ZFDu9YSJvW/Senior-Data-Analyst?source=GS

## About the Role

* Bachelor's or master's degree in a quantitative field such as computer science, systems engineering, mathematics, statistics, or a related technical discipline.
 * At least 5 years of professional experience in data analytics, business intelligence, or analytics engineering.
 * At least 4 years of hands-on experience using Tableau to design, maintain, and audit complex dashboards and calculated fields.
 * Demonstrated experience with cloud data warehouses, specifically Google BigQuery and/or Snowflake.
 * Proven experience delivering BI migration, dashboard refactoring, or semantic data modeling projects., * Advanced Tableau Engineering: Deep understanding of complex Tableau calculations, parameters, table calculations, and Level of Detail (LOD) expressions (FIXED, INCLUDE, EXCLUDE), with the ability to unpack workbook logic.
 * Cloud Data Warehousing & Advanced SQL: Strong expertise in writing performant, modular SQL (CTEs, window functions) and structuring data within Google BigQuery and/or Snowflake.
 * Modern Transformations & Modeling: Hands-on experience with dbt for building curated data models and managing transformation workflows.
 * Semantic Layer & Modern BI Tools: Hands-on experience building semantic data models or curated topics in Omni Analytics or Looker Studio (formerly Google Data Studio) / Looker.
 * Data Validation & Quality Assurance: Rigorous approach to data reconciliation, checking data parity between legacy source reporting and modernized target models.
 * Communication & Stakeholder Management: Excellent verbal and written English communication skills (Full Professional / Advanced) to lead technical discussions, clarify metric definitions, and present solutions directly to stakeholders.
 * Ways of Working: Self-motivated, proactive problem-solver comfortable working independently in an Agile sprint-based environment.

## Description

We are seeking a talented and experienced Senior Data Analyst / Analytics Engineer with deep expertise in Tableau, Google BigQuery, Snowflake, and modern data modeling (dbt, Omni, or Looker Studio) to join our team. In this role, you will lead critical business intelligence modernization initiatives, reverse-engineering complex reporting logic from legacy dashboards and rebuilding high-performance analytical data models to power executive decision-making., As a Senior Data Analyst / Analytics Engineer, you will work closely with technical leads, product managers, and business stakeholders to modernize legacy BI environments.

You will be responsible for deconstructing legacy dashboard architectures, extracting embedded business rules and Level of Detail (LOD) calculations, and re-architecting those metrics into scalable SQL models and cloud semantic layers.

You will perform as a core data translator, bridging the gap between cloud data warehouses, semantic layers, and client-facing business teams., * Reverse-Engineer Reporting Logic: Audit and deconstruct legacy Tableau workbooks, deciphering nested formulas, calculated fields, and LOD expressions into clear business documentation.

 * Warehouse Data Modeling: Translate extracted business logic into scalable, maintainable SQL and transformation models using dbt across BigQuery and Snowflake.
 * Semantic Layer Implementation: Build and configure curated data models, metrics, and self-service topics in Omni Analytics (or Looker Studio).
 * Data Parity & Governance: Implement thorough data validation checks to ensure metrics produced in the modern semantic layer match legacy benchmarks with 100% precision.
 * Stakeholder Alignment: Collaborate with technical teams, data engineers, and business leaders to define canonical KPI definitions and document data dictionaries.
 * Best Practices & Documentation: Establish reusable modeling standards, documentation, and maintainable BI pipelines for long-term platform health.