Data Engineer

Key Responsibilities

• Collect, clean, and analyze large datasets to identify trends and business opportunities
• Build interactive dashboards and reports using tools like Tableau, Power BI, and MS Excel
• Perform ad-hoc data analysis to support business and product teams
• Develop and monitor KPIs to track performance across business functions
• Collaborate with engineering teams to define data pipelines and ensure data quality
• Create and present data stories that simplify complex findings for non-technicalstakeholders
• Contribute to predictive modeling, risk modelling, segmentation, and automationinitiatives using Python, SQL, and PySpark
• Apply statistical and analytical techniques such as logistic regression, hypothesis testing, and feature engineering for business problem-solving
• Experience in marketing analytics, campaign measurement, and customer journeyanalysis.
• Exposure to risk modelling in NBFCs or Banks.
• Experience working with financial data and business risk indicators.
• Excellent analytical thinking, communication, and storytelling abilities.
• Exposure to machine learning techniques and Python API frameworks.
• Familiarity with workflow automation tools (e.g., Airflow).
• Bachelors degree in Engineering, Computer Science, Statistics, or a related field.Strong proficiency in SQL and PostgreSQL for complex queries and data manipulation.
• Advanced skills in Python for data analysis and automation, including libraries such as Pandas, NumPy, and Scikit-learn.
• Hands-on experience with PySpark for large-scale data processing and analytics.
• Expertise in data visualization tools like Tableau or Looker for creating meaningfuldashboards.
• Experience with data cleaning, feature engineering, modelling, and analytics workflows.
• Strong understanding of statistical methods, logistic regression, and data validationtechniques