SQL & Pipelines

Run BigQuery SQL queries and explore datasets and metadata

Query Snowflake databases, schemas and tables and run SQL through Composio

Add filter widgets and optional SQL blocks to native Metabase questions

Write Metabase queries with filters, aggregations, breakouts and joins

Write analytics SQL against a warehouse with consistent conventions and review steps

Build production Apache Airflow DAGs with operators, sensors and tests

Validate data quality with Great Expectations, dbt tests and data contracts

Organize dbt models, tests and incremental strategies for analytics engineering

Speed up Apache Spark jobs with partitioning, caching and shuffle tuning

Speed up slow SQL queries with indexing strategies and EXPLAIN plan analysis

Audit Python and BigQuery pipelines for cost safety, idempotency and readiness

Design and build Microsoft Fabric Lakehouses with schemas, shortcuts and access control

Create and validate Snowflake semantic views with the snow CLI

Compare SQL Server tables across instances to catch row mismatches and schema drift

Sync warehouse table columns onto person and group properties for flags and cohorts

Audit project endpoints for staleness, failed materializations and unused versions

Find broken or degraded warehouse sources, syncs and webhook channels in one audit

Find failed materialized views and flag stale or unused ones that waste compute

Add not-null, uniqueness, freshness and custom HogQL checks to warehouse tables

Copy an endpoint to another project or duplicate it under a new name

Create a query endpoint with the right query kind, variables and freshness settings

Find why a data quality check failed, inspect the matching rows and recommend a fix

Diagnose slow or costly endpoints and propose caching, materialization or query fixes

Diagnose failing or stuck warehouse syncs and recommend the right recovery action

Export events, persons, sessions or HogQL results as Parquet or JSONLines files

Read endpoint execution logs to diagnose failed runs, cache misses and odd row counts

Preview, roll back and deactivate endpoint versions without bumping query history

Model activation rate and activated-user flags on warehouse views or a dbt project

Model conversion rates, funnel steps, drop-off and time to convert in warehouse views

Build dimension and lookup tables for a star schema like country, currency and date

Model retention, stickiness and lifecycle metrics on warehouse views or dbt

Model MRR, ARR, churn, ARPU and LTV from warehouse revenue data

Set up shared foundations for warehouse views, materialized views or a dbt project

Review warehouse materialization and certification suggestions and act on them

Connect any REST API to the warehouse by writing a JSON manifest with no code

Connect Postgres, Stripe, HubSpot, Snowflake and other sources to the warehouse

Map a warehouse table or view onto person properties usable in flags and cohorts

Watch CDP destinations, batch exports and hog flows for delivery failures and stalls

Watch warehouse syncs and views for failures, silent staleness and row-count cliffs

Suggest external revenue, CRM and billing data to import and join with product events

Change a warehouse table sync type, incremental field, keys and frequency

Write correct, performant SQL across major warehouse dialects

Write optimized SQL for Snowflake, BigQuery, Postgres and other dialects

Manage BigQuery datasets, tables and jobs with SQL and BigQuery ML

Build CocoIndex ETL flows that embed documents into vector databases

Design ETL pipelines, data models and quality checks with Airflow, dbt and Spark

Write and run SQL from simple SELECTs to multi-table joins and aggregations

List tables, columns, keys and relationships to map a database schema

Build ETL and ELT pipelines with Airflow, dbt, Spark and Kafka