Chinni A

Data Analyst Intern

Hyderabad, India 0+ yrs exp 81 · Excellent

About

Data Analyst Intern with hands-on experience analysing 25,000+ records using SQL, Python, PySpark, and Databricks. Skilled in data cleaning, KPI development, dashboard creation, forecasting, geospatial analysis, and workflow automation. Built interactive AI/BI dashboards, validated complex datasets, and developed reliable reporting pipelines using Delta Lake and Lakeflow Jobs. Strong ability to translate raw data into clear insights that support business and operational decisions.

Skills & Expertise (30)

Python Advanced

8.5/10

Years Exp

SQL Advanced

8.5/10

Years Exp

PySpark Advanced

8.0/10

Years Exp

Data Cleaning Advanced

8.0/10

Years Exp

Exploratory data analysis Advanced

8.0/10

Years Exp

KPI development Advanced

8.0/10

Years Exp

Data Validation Advanced

8.0/10

Years Exp

ETL Pipelines Advanced

8.0/10

Years Exp

AWS Intermediate

7.5/10

Years Exp

Postgresql Intermediate

7.5/10

Years Exp

Jupyter Notebook Intermediate

7.0/10

Years Exp

GitHub Intermediate

7.0/10

Years Exp

Excel Intermediate

7.0/10

Years Exp

SQL Server Schema Validation RMSE MAE Time-Series Forecasting Delta Lake Databricks Interactive Dashboards BI Dashboards RANK LAG Aggregations Window Functions CTEs Joins Data Transformation Trend Analysis

Work Experience

Data Analyst Intern

Databricks

Feb 2026 - Present

Collaborate with mentors, analysts, and technical stakeholders in an Agile environment to support business intelligence reporting, dashboard development, and data-driven decision-making initiatives. Analyze 25,000+ public data records using SQL, Python, PySpark, and Databricks to identify market trends, performance patterns, location concentration, and category-level business insights. Develop ETL workflows using CSV files, REST API data, Jupyter Notebook, SQL, Python, and PySpark to extract, clean, transform, and prepare structured datasets for reporting automation and data visualization. Build and maintain interactive BI dashboards with KPI cards, trend charts, ranked tables, maps, and filters, enabling users to monitor key metrics by geography, category, unit type, and reporting period. Design 12 business KPIs covering rental growth, price premiums, permit volume, duration, location concentration, and category mix to improve performance tracking, reporting consistency, and market comparison. Improve data quality and reporting reliability by standardizing categories, parsing timestamps, handling missing values, validating duplicate records, and using Excel for reconciliation and reporting checks. Apply advanced SQL techniques, including joins, aggregations, window functions, LAG(), RANK(), and COUNT(DISTINCT), to support statistical analysis, root cause analysis, market ranking, and accurate business reporting. Support data modeling and reporting-ready data structures across Databricks, PostgreSQL, and SQL Server, contributing to scalable analytics, business intelligence, and data warehousing practices. Support cloud-based analytics workflows using AWS and Databricks, contributing to scalable data processing, dashboard automation, reporting automation, and organized analytics delivery. Create 12-month predictive analytics and forecasting workflows using Databricks forecasting capabilities, chronological holdout validation, MAE, and RMSE to support business planning with forward-looking trend estimates. Document KPI definitions, analysis methods, validation steps, dashboard logic, assumptions, limitations, and reporting best practices to support repeatable processes, knowledge sharing, and data governance.