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# Data Analyst

- **Company:** TekWissen LLC
- **Location:** New York, NY, United States (Remote available)
- **Experience:** Experienced
- **Salary:** $156,000 - $176,800
- **Contract:** Temporary contract
- **Skills:** Microsoft Excel, Artificial Intelligence, Amazon Web Services, Data Analysis, Microsoft Azure, BigQuery, Cloud Computing, Internet Security, Python (Programming Language), Machine Learning, Raw Data, Power BI, SQL Databases, Tableau (Software), Retrieval-Augmented Generation, Advanced Reports, Data Analytics, Nintex, Looker Analytics
- **Published:** November 11, 2026
- **Apply:** https://www.careerbuilder.com/job-details/data-analyst-seattle-wa--53c43d20-de05-4baa-8c88-31aa8fe02eec

## About the Role

Data Analysis, Internet Security, Workforce Management, Supply Chain, Artificial Intelligence (AI), Finance, Data Analysis, Automation, Analysis Skills, Leadership, * SQL / BigQuery

 * Python / R / Apps Script / n8n
 * Tableau / Power BI / Looker
 * Excel / Google Sheets
 * Strong executive-level business communication

Nice-to-Have Skills

 * GenAI / RAG / Agents
 * Machine Learning / Statistics
 * AWS / Azure / GCP / FinOps
 * Financial / Value Modeling.

Candidate Profile

 * Typically 3+ years in data analytics, BI, or analytics-heavy consulting.
 * Evidence of automating manual reporting processes.
 * Experience presenting recommendations to senior stakeholders.
 * Cloud, FinOps, and GenAI certifications are beneficial.

## Description

* Business-savvy data analyst who bridges data, automation, and decision-making.
 * The role goes beyond building dashboards: the analyst owns the journey from raw data through analysis and automated reporting to actionable recommendations for leadership.

Core Responsibilities

 * Automate recurring reporting end to end, including data collection, reconciliation, calculations, narrative, and distribution.
 * Build dashboards that provide leaders with live visibility across programs and business segments.
 * Validate data quality and maintain reliable pipelines.
 * Use analytics, forecasting, scoring, and classification to identify savings, risk, and growth opportunities.
 * Translate findings into business cases, recommendations, and executive briefings.
 * Apply AI and agentic tools to reduce manual work while maintaining human review and auditability.
 * Work across business, engineering, and data science teams to implement recommendations, not simply perform analysis.

Operating Model

 * Source & Validate
 * Analyze & Model
 * Automate Reporting
 * Visualize
 * Advise
 * AI and agents support the lifecycle across all stages.