Data Skills

End-to-end ML workflow automation.

Neo4j and graph query patterns.

Modern data lake best practices.

Data validation and cleansing.

Database operations, vector search, storage.

Distributed computing patterns.

Forecasting and trend analysis.

Creating insightful charts and dashboards.

MongoDB, Redis, and Key-Value stores.

Query GitHub repository documentation.

ETL and data pipeline orchestration.

Edge-ready Postgres databases.

SQL analysis, data processing.

Real-time analytics and OLAP.

PostgreSQL performance and features.

Schema normalization and optimization.

Real-time data stream processing.

Creating impactful ML features.