Product Data Analyst (L3)
IndiaFull-TimeMiddle
Salary not disclosed
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Job Details
- Experience
- 4+ years
- Required Skills
- PythonSQLBusiness IntelligenceTableauData engineeringData visualizationData modelingData analyticsLooker
Requirements
- 4+ years of experience in data analytics, business intelligence, or quantitative analysis roles.
- Proven ability to translate complex data into business insights.
- Strong hands-on experience with SQL or relational databases, including querying, optimization, and quality assurance.
- Proficiency in data visualization tools such as Looker and/or Tableau to build insightful dashboards.
- Strong structured problem-solving skills with the ability to break down ambiguous problems into clear analytical approaches.
- Demonstrated experience owning analytics projects end-to-end with minimal supervision.
- Excellent organizational and project management skills with the ability to manage multiple priorities simultaneously.
- Strong communication skills with the ability to translate data findings into clear narratives for stakeholders.
- Familiarity with AI tools for analytics acceleration is a plus.
- Exposure to Python, statistical methods, or data engineering concepts is a plus.
Responsibilities
- Own end-to-end analytics initiatives, from defining ambiguous business problems to delivering clear, actionable insights that drive product decisions.
- Partner closely with product managers and stakeholders to define KPIs, build dashboards, and support product strategy with data.
- Write, optimize, and validate complex SQL queries to extract and transform data for analysis and reporting.
- Develop and maintain dashboards and visualizations using BI tools such as Looker and Tableau to communicate insights effectively.
- Ensure data consistency by documenting metrics, datasets, and analytical models to establish a single source of truth.
- Manage multiple concurrent analytics projects with strong prioritization, planning, and timely delivery.
- Maintain high standards of data quality through rigorous validation, QA processes, and continuous monitoring.
- Leverage AI tools to enhance productivity in data analysis, SQL development, and documentation.
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