ETL/ELT pipelines, warehouse modeling, dbt transforms, streaming ingestion, and SQL across dialects. Skills for moving and shaping data at scale.
Related searches: claude code skills for data engineers, AI data engineering skills, claude ETL pipeline skills, claude code data warehouse skills.
Expert data engineer for ETL/ELT pipelines, streaming, data warehousing. Activate on: data pipeline, ETL, ELT, data warehouse, Spark, Kafka, Airflow, dbt, data modeling, star schema, streaming data, b
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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 architectu
engineering
World-class data engineering skill for building scalable data pipelines, ETL/ELT systems, and data infrastructure. Expertise in Python, SQL, Spark, Airflow, dbt, Kafka, a — from ricardonevesbraga/flow
engineering
World-class data engineering skill for building scalable data pipelines, ETL/ELT systems, and data infrastructure. Expertise in Python, SQL, Spark, Airflow, dbt, Kafka, a — from bg-szy/TOP-SKILLS
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ETL/ELT pipeline design, data flow visualization, batch/streaming selection, and Kafka/Airflow/dbt design. Use when building data pipelines or managing data quality.
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Expert data engineer specializing in building reliable data pipelines, lakehouse architectures, and scalable data infrastructure. Masters ETL/ELT, Apache Spark, dbt, streaming systems, and cloud data
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Build scalable data pipelines, modern data warehouses, and real-time streaming architectures. Implements Apache Spark, dbt, Airflow, and cloud-native data platforms.
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Review or design a data pipeline architecture. Assesses ingestion pattern, transformation design, orchestration, idempotency, freshness SLAs, data contracts at boundaries, dbt test coverage, lineage,
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Syncing Rails Postgres to a data warehouse (Snowflake, BigQuery, Redshift) — Fivetran / Airbyte / Hightouch / Stitch / Census / CDC via Debezium, when ELT beats ETL, dbt for transformation, reverse-ET
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Use when building data pipelines, ETL jobs, event processors, message-broker producers/consumers, application caching layers, database migrations, BigQuery queries, or event-sourcing handlers in TypeS
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Import data into the AWS data lake from S3 files, local uploads, JDBC databases (Oracle, SQL Server, PostgreSQL, MySQL, RDS, Aurora), Amazon Redshift, Snowflake, BigQuery, DynamoDB, or existing Glue c
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MaestroQA platform help — conversation data QA platform with customizable scorecards, AI-powered coaching workflows, conversation analytics (AskAI), reverse-ETL to CRM/Slack/data warehouses, CSAT inge
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Develop Lakeflow Spark Declarative Pipelines (formerly Delta Live Tables) on Databricks. Use when building batch or streaming data pipelines with Python or SQL. Invoke BEFORE starting implementation.
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Conception de pipelines de données robustes et scalables. Se déclenche avec "data pipeline", "pipeline de données", "batch processing", "stream processing", "Apache Spark", "Airflow", "dbt", "data eng
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Docker for data engineering environments — multi-stage Dockerfiles for dbt/Spark/Airflow images, BuildKit layer caching (--mount=type=cache), private registries (ghcr.io/Harbor), docker buildx multi-p
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Implement end-to-end Medallion Architecture (Bronze/Silver/Gold) lakehouse patterns in Microsoft Fabric using PySpark, Delta Lake, and Fabric Pipelines. Use when the user wants to: (1) design a Bronze
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Delta Lake, Apache Iceberg, Hudi for ACID transactions on object storage. Activate on: lakehouse, Delta Lake, Iceberg, Hudi, table format, ACID on S3, time travel, data lake, open table format. NOT fo
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Creates, configures, and updates Databricks Lakeflow Spark Declarative Pipelines (SDP/LDP) using serverless compute. Handles streaming tables, materialized views, CDC, SCD Type 2, and Auto Loader inge
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Stream rows continuously into Apache Iceberg tables on S3 Tables (or standard Iceberg on a general purpose bucket) using Amazon Data Firehose with IcebergDestinationConfiguration. Covers the Firehose
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Python DAG workflow orchestration using Apache Airflow for data pipelines, ETL processes, and scheduled task automation
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Expert guidance for Ibis, the Python dataframe library that provides a pandas-like API but generates SQL for execution on any backend — DuckDB, PostgreSQL, BigQuery, Snowflake, Spark, and more. Helps
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Build a Clari forecast export pipeline to your data warehouse. Use when exporting forecast calls, quota data, and CRM totals from Clari to Snowflake, BigQuery, or a local database. Trigger with phrase
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Ingest data into ClickHouse from webhooks, Kafka, and streaming sources with batching, dedup, and exactly-once patterns. Use when building data ingestion pipelines, consuming webhook payloads, or inte
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ALWAYS USE when working with Dagster assets, resources, IO managers, schedules, sensors, or dbt integration. CRITICAL for: @asset decorators, @dbt_assets, DbtCliResource, ConfigurableResource, IO mana
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Create data pipeline and analytics architecture diagrams using PlantUML syntax with database/analytics stencil icons. Best for ETL pipelines, data lakes, real-time streaming, data warehousing, and BI
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Design an ETL/ELT data pipeline specification. Use when asked to design a data pipeline, spec an ETL or ELT process, document a data ingestion workflow, or plan a data integration. Produces a complete
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Running experiments out of the data warehouse instead of via dedicated experiment platforms. SQL-based assignment, exposure logging discipline, metric definitions in dbt models, statistical analysis i
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Snowflake, BigQuery, clustering, partitioning, and materialized views for warehouse performance. Activate on: Snowflake, BigQuery, Redshift, query optimization, clustering, partitioning, materialized
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Execute Databricks primary workflow: Delta Lake ETL pipelines. Use when building data ingestion pipelines, implementing medallion architecture, or creating Delta Lake transformations. Trigger with phr
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Apache Airflow, dbt, Prefect, Dagster, and modern data orchestration for production data pipelines
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