data▌

Senior-level data engineering expertise for building scalable pipelines, ETL systems, and production data infrastructure. \n \n Covers advanced patterns across data pipeline architecture, modeling, and DataOps with distributed computing frameworks (Spark, Airflow, dbt, Kafka) and modern data stack tools (Databricks, BigQuery, Snowflake) \n Includes production deployment patterns for scalable data processing, ML model serving with low latency, and real-time inference with auto-scaling and monitor

Fetches financial and market data from Yahoo Finance using the yfinance Python library.

Optimize Entity Framework Core queries by fixing N+1 problems, choosing correct tracking modes, using compiled queries, and avoiding common performance traps. Use when EF Core queries are slow, generating excessive SQL, or causing high database load.

Analyze Excel files to identify data structure, quality issues, format inconsistencies, and statistical patterns. Generate comprehensive markdown reports with actionable insights for data cleaning and improvement.

Provides expertise in efficient CSV file processing, data cleaning, and transformation. Handles large files, encoding issues, malformed data, and performance optimization for tabular data workflows.

Automatically analyzes CSV files and generates comprehensive statistics with intelligent, context-aware visualizations. \n \n Intelligently adapts analysis to data type (sales, customer, financial, operational, survey) by inspecting columns first, then runs relevant analyses without asking \n Generates only applicable visualizations: time-series plots for date columns, correlation heatmaps for multiple numeric columns, distributions for categorical data \n Provides complete output in one respons

Transform raw data into compelling narratives that drive decisions and inspire action.

Persistence layer patterns for Spring Data JPA repositories, entities, queries, and advanced features. \n \n Create repository interfaces extending JpaRepository with derived queries, custom @Query methods, and automatic CRUD operations \n Configure entity relationships (one-to-one, one-to-many, many-to-many) with appropriate cascade types and fetch strategies \n Implement pagination, sorting, database auditing with timestamps and user tracking, and transaction management \n Optimize performance

Dataset exploration, cleaning, statistical analysis, and visualization in Python or SQL. \n \n Supports CSV, JSON, and SQL data sources with pandas DataFrames and direct database queries \n Covers the full analysis pipeline: data loading, missing value handling, outlier detection, grouping, correlation analysis, and pivot tables \n Includes visualization templates for histograms, boxplots, heatmaps, and time series using matplotlib and seaborn \n Generates structured markdown reports with datase

Network requests, API calls, and data fetching for Expo apps with caching, error handling, and offline support. \n \n Covers fetch API, React Query, error handling patterns, token management with expo-secure-store, and retry logic with exponential backoff \n Includes offline-first strategies using NetInfo and React Query persistence, plus environment variable configuration with EXPO_PUBLIC_ prefix for client-side URLs \n Supports Expo Router data loaders ( useLoaderData ) for route-level data lo