Business Intelligence Analyst
United StatesFull-TimeMiddle
Salary80,000 - 100,000 USD per year
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Job Details
- Experience
- 3+ years
- Required Skills
- PythonSQLData AnalysisETLMicrosoft Power BITableauData modelingLooker
Requirements
- Bachelor’s degree in a quantitative field such as mathematics, statistics, data analytics, or equivalent experience.
- 3+ years of experience in business intelligence, data analysis, statistics, or a related analytical role.
- Strong proficiency in SQL, including writing and optimizing complex queries.
- Solid understanding of data modeling concepts such as star and snowflake schemas.
- Hands-on experience with BI and visualization tools such as Power BI, Looker, or Tableau.
- Familiarity with ETL processes and collaboration with data engineering teams.
- Exposure to Python or other scripting languages is a plus.
- Strong analytical thinking, problem-solving skills, and attention to data accuracy.
- Ability to communicate insights clearly and translate data into business recommendations.
- Experience supporting data governance, data quality, and metadata management practices.
Responsibilities
- Owning medium-complexity analytics projects from requirements definition through delivery, in close collaboration with business stakeholders.
- Analyzing structured and unstructured data from multiple sources to identify trends, patterns, and actionable insights.
- Designing and building dashboards and reports using BI tools such as Power BI or Looker to support business decision-making.
- Translating business requirements into technical specifications, including KPIs, calculations, and business rules.
- Conducting exploratory data analysis and hypothesis testing to validate assumptions and support strategic decisions.
- Collaborating with data engineering teams to ensure proper data flows, structures, and integrations for analytics use cases.
- Contributing to data governance by maintaining definitions, metadata, and documentation of data lineage.
- Supporting data quality initiatives to improve accuracy, consistency, and trust in organizational data.
- Working with cross-functional teams to identify opportunities for process optimization, revenue growth, and improved customer experience.
- Applying basic data modeling concepts and supporting ETL and data pipeline development efforts.
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