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Found 44 Skills
Primary entry point for building, managing, and orchestrating data pipelines on Google Cloud. Guides users to the appropriate skill for dbt, Dataflow (Apache Beam), Dataform, Spark (Dataproc Serverless), BigQuery Data Transfer Service (DTS) or orchestration pipeline using Cloud Composer. Clarify requirements and resolve ambiguity for creating, updating and running data pipelines.
Create efficient data pipelines with tf.data
Use this skill for data pipeline work — ingestion with dlt, transformations with sqlmesh, analytics with DuckDB/MotherDuck, DataFrames with polars, notebooks with marimo, and project management with uv.
Use this skill when building data pipelines, ETL/ELT workflows, or data transformation layers. Triggers on Airflow DAG design, dbt model creation, Spark job optimization, streaming vs batch architecture decisions, data ingestion, data quality checks, pipeline orchestration, incremental loads, CDC (change data capture), schema evolution, and data warehouse modeling. Acts as a senior data engineer advisor for building reliable, scalable data infrastructure.
Query blockchain data across 40+ chains, create AI agents for crypto analytics, and build automated data pipelines. Use when working with blockchain data, crypto wallets, DeFi protocols, NFTs, token transfers, or on-chain analytics. Requires the Flipside CLI (https://docs.flipsidecrypto.xyz/get-started/cli).
Expert in data pipelines, ETL processes, and data infrastructure
Expert in Apache Kafka, Event Streaming, and Real-time Data Pipelines. Specializes in Kafka Connect, KSQL, and Schema Registry.
Complete guide for Apache Airflow orchestration including DAGs, operators, sensors, XComs, task dependencies, dynamic workflows, and production deployment
Expert-level Apache Airflow orchestration, DAGs, operators, sensors, XComs, task dependencies, and scheduling
Reporting pipelines for CSV/JSON/Markdown exports with timestamped outputs, summaries, and post-processing.
Binary streaming between workers via channels. Use when building data pipelines, file transfers, streaming responses, or any pattern requiring binary data transfer between functions.
Patterns for efficient ML data pipelines using Polars, Arrow, and ClickHouse. TRIGGERS - data pipeline, polars vs pandas, arrow format, clickhouse ml, efficient loading, zero-copy, memory optimization.