Data Analyst

Overview

We are seeking a skilled Data Analyst with 3–5 years of experience in transforming raw data into meaningful insights that drive business decisions. The ideal candidate will have strong analytical thinking, hands-on experience with data querying and visualization, and a solid understanding of data pipelines and statistical concepts.

While the primary focus of this role is analysis, reporting, and insights, the candidate should also have exposure to data science concepts such as feature engineering, basic predictive modeling, and collaboration with Data Science and ML Engineering teams.

You will work closely with business stakeholders, product managers, and data scientists to analyze trends, identify patterns, and support data-driven decision-making across the organization.

Key Responsibilities

• Analyze large, complex datasets to identify trends, patterns, and actionable insights for business teams.
• Design, build, and maintain dashboards and reports using BI and visualization tools to track KPIs and operational metrics.
• Write optimized SQL queries to extract, transform, and analyze data from relational and analytical data stores.
• Perform data cleaning, preprocessing, and exploratory data analysis (EDA) using Python (Pandas, NumPy).
• Conduct ad-hoc analysis to answer business questions and present findings to stakeholders in a clear, concise manner.
• Apply statistical techniques (descriptive statistics, hypothesis testing, correlation analysis) to interpret data accurately.
• Support data quality checks, anomaly detection, and data consistency across multiple data sources.
• Work with engineering teams to understand data pipelines and ensure reliable data availability for analysis.
• Document data definitions, metrics logic, and analytical methodologies for transparency and reuse.
• Stay updated with emerging analytics tools, data visualization techniques, and basic ML concepts relevant to analytics.
  
  

• 3–5 years of experience as a Data Analyst or Analytics Engineer.
• Strong proficiency in SQL for complex queries, joins, and performance optimization.
• Hands-on experience with Python for data analysis (Pandas, NumPy).
• Experience with data visualization and BI tools (Power BI, Tableau, Looker, or similar).
• Solid understanding of data analysis techniques, KPIs, and business metrics.
• Working knowledge of statistics (mean, variance, regression basics, hypothesis testing).
• Ability to translate business problems into analytical questions and insights.
• Familiarity with data warehousing concepts and analytical data models.
• Good communication skills with the ability to explain insights to non-technical stakeholders.
• Strong problem-solving mindset and attention to detail.
  
  

• Exposure to machine learning concepts such as regression, classification, and clustering (hands-on or academic).
• Experience supporting predictive analytics or data science initiatives alongside ML teams.
• Familiarity with big data or distributed data processing tools (e.g., Spark) is a plus.
• Knowledge of cloud-based data platforms (AWS, Azure, or GCP).
• Experience with version control tools like GitHub.
• Degree in Computer Science, Data Science, Statistics, Engineering, or a related field.