Diksha Kumari

AI & Data Science Enthusiast | Python, Flask, Django, SQL | Data Analytics Intern at HCL & Proxenix | Certified by IBM, Deloitte & Tata Forage · Kakori, Uttar Pradesh, India

I'm a Data Engineer at Cognifyz Technologies with 3 years of experience building reliable, scalable data solutions that drive meaningful outcomes. I hold myself to a high standard — balancing analytical rigor with a people-first approach — ensuring I'm both a dependable team player and an independent problem-solver. Ambitious by nature, I take ownership of my work end-to-end and make it a point to stay approachable and open, because I believe the best engineering happens when collaboration and accountability go hand in hand.

Experience

Data engineering

Cognifyz Technologies · Jun 2025 – Jul 2025

Not yet confirmed

• Learned and applied core data engineering concepts including ETL pipelines, data warehousing, and data integration.
• Worked with tools such as SQL, Python, and Apache Airflow to extract, transform, and load structured and unstructured data.
• Gained experience with cloud platforms and data storage solutions like AWS S3, Google BigQuery, or Snowflake (based on actual tools used).
• Built automated data pipelines and ensured data quality and consistency across sources

Data Analyst

HCLTech · Oct 2024 – Mar 2025

Not yet confirmed

• Utilized Python for data cleaning, transformation, and exploratory data analysis (EDA) using libraries like Pandas, NumPy, and Matplotlib.
• Analyzed large datasets to extract actionable insights and supported business decision-making processes.
• Created data visualizations and dashboards to communicate findings effectively to the team.
• Assisted in building and optimizing predictive models using scikit-learn.
• Collaborated with cross-functional teams to understand data requirements and present analytical solutions.

Machine Learning Engineer

Acmegrade · Nov 2024 – Jan 2025

Not yet confirmed

• Worked on real-time datasets to implement ML models using Python, scikit-learn, and Pandas.
• Explored concepts such as model evaluation, data preprocessing, feature selection, and cross-validation.
• Completed end-to-end ML projects including data analysis, model building, and performance optimization.
• Strengthened understanding of AI concepts and applied ML techniques in practical scenarios.