ETL in Data & Analytics

Open, edit, and create Excel and CSV files with formulas, formatting, and data cleaning.

Create and deploy data engineering jobs on Databricks using notebooks, Python, SQL, or pipelines.

Build batch or streaming data pipelines on Databricks with Python or SQL.

Build real-time data ingestion clients that write directly to Databricks Delta tables.

Manage Apache Iceberg tables and enable cross-platform data sharing on Databricks.

Build production streaming pipelines with Kafka, joins, and real-time processing.

Build custom Python connectors to read and write data between Spark and external systems.

Design and operate Snowpipe disaster recovery patterns on Azure ADLS Gen2.

Work with annotated data matrices and .h5ad files for single-cell analysis.

Manage customer health, predict churn, handle RFPs, and streamline sales operations.

Analyze sales pipeline health, forecast accuracy, and go-to-market efficiency for SaaS teams.

Enable C-suite agents to query each other, coordinate analysis, and prevent communication loops.

Build tools to import, inspect, and migrate DeepChat chat data and configurations.

Turn website data extraction into reusable, deployable skills with automatic API discovery.

Retrieve structured product details from Amazon using an ASIN number.

Create, edit, and validate n8n workflows and automation configurations.

Capture your project's exact language versions, packages, and dependencies for reproducible research.

Design multi-agent systems with context isolation, coordination, and parallel execution.

Classify and resolve conflicts when ingesting data bundles.

Import live database schemas and create semantic layers from snapshot files.

Extract dashboards, looks, and explores from Looker into structured knowledge.

Map LookML views and explores into KTX semantic layer sources.

Extract experiments from AI agent logs and prepare them for academic paper writing.

Map dbt models and sources into semantic layer overlays with column metadata.

Identify recurring SQL query patterns across database tables for analysis.

Convert SQL table usage data into typed evidence for schema projection.

Extract text from PDF files with support for multiple languages.

List, query, version, and clean up model data artifacts with flexible filtering.

Build modular data processing workflows with batch jobs and API integration.

Consolidate IBRK trade CSV files into summary reports organized by symbol and date.

Optimize ClickHouse queries and design high-performance analytical databases.

Search tweets, lookup users, extract followers, and manage X posts via Xquik API.

Build and manage semantic layers that map databases to business metrics and dimensions.

Execute substantial development tasks with isolated workspaces, testing phases, and quality gates.

Build and optimize Snowflake data pipelines, SQL queries, and AI-powered applications.