Data Analyst at Qode
Basking Ridge, NJ 07920, USA -
Full Time


Start Date

Immediate

Expiry Date

23 Nov, 25

Salary

0.0

Posted On

23 Aug, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

Data Modeling, Sql, Tableau, Reporting, Data Analysis, Data Analytics, Azure, Computer Science, Aws, Google Cloud, Data Manipulation, Looker, Power Bi, Data Studio, Communication Skills, Google Sheets, Statistics, Business Intelligence

Industry

Information Technology/IT

Description

REQUIRED QUALIFICATIONS:

  • Bachelor’s degree in Data Analytics, Statistics, Computer Science, Engineering, or related field.
  • 2–5 years of professional experience in data analytics or business intelligence.
  • Strong proficiency in SQL for querying and data manipulation.
  • Experience with data visualization tools (Looker, Tableau, Power BI, or Data Studio).
  • Proficiency in Excel / Google Sheets for quick analysis and reporting.
  • Strong understanding of data modeling, KPIs, and metrics tracking.
  • Experience with cloud platforms (Google Cloud, AWS, or Azure) is a plus.
  • Familiarity with Python/R for data analysis preferred.
  • Excellent problem-solving, analytical thinking, and communication skills.
Responsibilities
  • Collect, clean, and analyze structured and unstructured datasets from multiple sources (databases, cloud storage, APIs, spreadsheets).
  • Design and build dashboards, reports, and visualizations using BI tools (e.g., Looker, Tableau, Power BI, Data Studio).
  • Develop and maintain SQL queries, scripts, and pipelines to extract and transform data for analytics use cases.
  • Perform exploratory data analysis (EDA), descriptive statistics, and trend analysis to identify business patterns and opportunities.
  • Collaborate with stakeholders across product, business, and engineering teams to translate requirements into data insights.
  • Support KPI definition, monitoring, and reporting to track business performance.
  • Ensure data integrity, accuracy, and governance across reports and dashboards.
  • Recommend improvements in data collection methods, data quality, and reporting automation.
  • Contribute to predictive and prescriptive analytics initiatives by preparing data and assisting data science teams.
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