ETL/ELT pipelines, warehouse modeling, dbt transforms, streaming ingestion, and SQL across dialects. Skills for moving and shaping data at scale.
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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
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Forge playbook for data engineering — ETL/ELT, dbt/SQL pipelines. Use for data pipeline, warehouse, Snowflake, BigQuery, dbt, incremental load, schema contract, data quality, PII.
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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
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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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데이터 엔지니어링·클라우드 글의 기술 깊이를 만든다. GCP(BigQuery/Dataflow/Pub-Sub/Composer)와 OSS 데이터 스택(Spark/Airflow/Kafka/Flink/dbt/Iceberg)의 아키텍처·실행 가능한 코드·트레이드오프·실제 스키마를 02_sme_notes.md에 작성한다. AWS/Snowflake/Databricks는
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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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Expert guide for Data Pipelines, ETL/ELT, and Analytics Engineering. Covers dbt, Apache Airflow, Dagster, BigQuery, ClickHouse, and DuckDB / Panduan ahli untuk Data Pipelines, ETL/ELT. Mencakup dbt, A
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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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Use this skill when running data workloads on Kubernetes: Spark on K8s, Airflow on K8s, Kafka on K8s, Strimzi, Spark Operator, Volcano, GPU scheduling, node pools, Karpenter, CSI storage for data work
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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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Use this skill when building or operating data lakes with Delta Lake, Apache Iceberg, or Apache Hudi. This skill enforces: table format selection, ACID transactions on object storage, time travel, sch
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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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Review Microsoft Fabric data engineering artifacts — Lakehouse and OneLake design, medallion (bronze/silver/gold) architecture, Spark notebooks and Spark job definitions, Data pipelines and Dataflows
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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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Use this skill when asked about data cost optimization, Snowflake cost management, BigQuery slot management, S3 storage tiering, warehouse cost optimization, FinOps for data, cloud data cost reduction
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Use this skill when designing end-to-end data platforms: data lake architecture, data lakehouse architecture, distributed storage (S3, ADLS, GCS), distributed compute (Spark, Trino, Presto), data cata
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Compute delta sets from warehouse high-water marks or snapshots so each run processes only new or changed records. Use this skill whenever the user mentions incremental load, high water mark, delta de
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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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Selects, investigates, and compares AWS object, file, and block storage services, and answers cost, performance, configuration, security, and troubleshooting questions about storage services. Applies
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