Hyderabad, Telangana, India
B.Tech graduate now working as a Data Analyst Trainee at Innomatics Research Labs — cleansing, standardizing, and linking large datasets, reporting through Power BI and SQL, and applying AI/ML and LLM tools to work faster and smarter.
The datasets I've worked with span operational, financial, and library-system records — checked for missing values, duplicate entries, inconsistent formatting, and mismatched relationships between tables, then cleansed, standardized, and re-validated using SQL and Python before reaching stakeholders.
Innomatics Research Labs — reviewed for missing values, duplicates, and formatting inconsistencies across relational tables.
SQL ETL project — pulled from multiple sources and consolidated into one standardized, query-ready dataset.
Library database — resolved redundancy and structural anomalies across a 10+ table relational schema.
Joins, subqueries, and aggregations used to cross-check and validate circulation records for accuracy.
-- about
I'm a B.Tech graduate in Electronics and Communication Engineering (2025) who moved into data work through hands-on internships. I started with a data science internship at CodSoft IT Solutions, where I got my first real exposure to analyzing operational data and building AI-driven solutions. After graduating, I joined Innomatics Research Labs as a Data Analyst Trainee, where I now work with datasets of 50,000+ records — going through them to catch missing values, duplicate entries, inconsistent formatting, and mismatched relationships between tables, then tracing each issue back to its source instead of just patching the output.
Day to day, my work sits between research and reporting: cleansing and standardizing raw data, linking records across relational tables with SQL joins and keys, running validation checks to confirm the fixes actually hold, and building Power BI dashboards that let stakeholders see what changed, why, and how much cleaner the data is afterward. Alongside the core analyst toolkit, I've been building applied skills in AI, Machine Learning, Large Language Models, and prompt engineering — using them to speed up research, automate repetitive cleansing steps, and explore smarter ways to surface patterns in data.
-- journey
From a data science internship, through graduation, to a data analyst traineeship — each step building on the last.
Analyzed 10,000+ operational records, applied cleansing and standardization techniques, and ran exploratory data analysis — my first hands-on introduction to applied AI and data analytics.
Completed my engineering degree while already building a portfolio of SQL and data-quality projects on the side.
Reviewing 50,000+ records for data quality, applying SQL-based linking and mapping across relational tables, and building Power BI dashboards to report findings to stakeholders.
-- experience detail
-- skills
Core analyst toolkit, plus the AI and LLM skills I'm actively applying to speed up research and analysis.
-- projects
-- education & certifications
-- feedback
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