Data Analyst

Sr.Data Analyst

Job Summary

This role is responsible for transforming large, complex datasets into meaningful insights, building dashboards, conducting advanced analytics, and supporting AI/ML use cases with structured and well-curated data.

Experience Level: 10+ years

Jd

• Analyze large structured and unstructured datasets to identify trends, patterns, correlations, and business insights.
• Build analytical models, conduct exploratory data analysis (EDA), and evaluate business KPIs.
• Develop actionable insights to support strategy, product decisions, and operational improvements.
• Create interactive dashboards and reports using BI tools (Looker, Tableau, Power BI, Data Studio).
• Automate recurring reports and ensure accuracy, consistency, and data freshness.
• Develop self-service analytic views for business stakeholders
• Conduct hypothesis testing, statistical modeling, forecasting, and clustering analyses.
• Support AI/ML teams with data profiling, feature validation, and dataset quality checks.
• Perform cohort analysis, funnel analysis, segmentation, churn modeling, and performance deep dives.
• Perform data validation, cleansing, anomaly detection, and root-cause analysis.
• Document data definitions, metric logic, dashboards, and analytical workflows.
• Partner with data engineering to improve data pipelines, schemas, and source integrations.
  
  

Technical Must-Haves

• 6–10 years of experience as a Data Analyst or Senior Analyst.
• Strong SQL (advanced joins, window functions, CTEs, optimization).
• Hands-on experience with BI/visualization tools: Looker, Tableau, Power BI, Data Studio.
• Strong proficiency with Python or R for analysis (pandas, numpy, visualization libraries).
• Experience working with data warehouses (BigQuery, Snowflake, Redshift).
• Deep experience performing EDA, statistical analysis, and KPI modeling.
• Experience supporting AI/ML projects or LLM evaluation datasets.
• Knowledge of Google Cloud data stack (BigQuery, Cloud Storage, Pub/Sub, Dataform).
• Familiarity with experimentation frameworks (A/B testing).
• Experience with ETL/ELT workflows and interacting with data engineering pipelines.