Snowflake plugins

Build production-ready data pipelines with Apache Airflow and dbt, manage scalable data warehouses, and implement vector search and RAG systems using embedding models and vector databases.

Perform business analysis workflows — KPI frameworks, predictive models, real-time dashboards, TAM/SAM/SOM calculations, and multi-year financial modeling for startups — using Python, SQL, and cloud data warehouses like Snowflake and BigQuery.

Build and manage end-to-end data analytics workflows: implement A/B testing with statistical rigor, design reliable analytics tracking, create interactive D3.js visualizations, architect scalable database schemas, and optimize SQL for cloud-native databases.

Apply structured playbooks across research, brand, build, and audit phases to execute the full website lifecycle—including accessibility audits, SEO optimization, content strategy, design systems, deployment runbooks, performance monitoring, and security baselines.

Manage AWS data lakes and analytics workflows: create and query Iceberg tables on S3 Tables, run Athena SQL across Glue and Redshift catalogs, ingest data from JDBC databases, Redshift, Snowflake, BigQuery, and DynamoDB, audit Glue Data Catalog assets, and store/query vector embeddings on S3 Vectors for semantic search and RAG.

Manage Apache Airflow pipelines end-to-end: author DAGs, run tests, diagnose failures, explore warehouse schemas, profile tables, trace data lineage, and deploy to production using the Astro CLI.

Guides data engineering projects through a structured Spec-Driven Development workflow with 58 specialized agents for pipeline design, schema modeling, SQL optimization, data quality, lakehouse architecture, and AI/ML infrastructure. Generates visual diagrams, HTML documentation, code reviews, and git-aware status reports.

Write geospatial SQL queries against BigQuery, Snowflake, Wherobots, and Postgres with automatic cost estimation, mandatory dry-run validation, and interactive map rendering of results via Dekart.

Profile, analyze, and QA data workflows across multiple SQL dialects — craft optimized queries, apply statistical methods, profile unfamiliar datasets, and catch common analysis pitfalls with a reproducible quality checklist.

Analyze and optimize cloud, AI, and SaaS costs across AWS, Azure, GCP, Databricks, and Snowflake with guidance on commitment management, rightsizing, cost allocation, and AI spend reduction.

Manage Omni Analytics instances, build and query semantic models, create dashboards, embed analytics in external apps, and optimize AI model accuracy — all via CLI and REST API.

Query and model data through the Honeydew semantic layer: define entities, metrics, and dimensions from Snowflake or BigQuery sources, validate them, and explore data with structured queries or natural language.

Connect Oracle AIDP Spark notebooks to 23+ enterprise data sources (Oracle DB, AWS S3, Azure ADLS, Salesforce, Snowflake, PostgreSQL, MySQL, etc.) for read/write data pipelines using JDBC, REST, and cloud-native connectors.

Automate Linux Foundation development workflows: scaffold Snowflake/dbt data sources, enforce post-commit code conventions with peer-review pattern audits, manage Git DCO signoff, PR review resolution, and cross-repo task routing.

Generate optimized SQL queries from natural language for BigQuery, PostgreSQL, MySQL, and Snowflake; perform cohort analysis on CSV/Excel user data to compute retention rates, visualize trends, and detect anomalies; evaluate A/B tests with statistical significance, confidence intervals, and launch recommendations.

Accelerates data platform delivery across the full lifecycle — requirements, design, development, testing, deployment, and enablement — by orchestrating AI agents and MCP servers for analytics instrumentation, dbt model management, platform migrations, semantic layer builds, and stakeholder reviews.

Manage Keboola data pipelines via CLI: initialize projects, sync configs bidirectionally with diff previews, and launch 10-agent AI audits for SQL quality, security, performance, financial logic, data architecture, PII detection, lineage mapping, and templatization readiness, generating prioritized reports and fixes.