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Found 17 Skills
Data engineering skill for building scalable data pipelines, ETL/ELT systems, and data infrastructure. Expertise in Python, SQL, Spark, Airflow, dbt, Kafka, and modern data stack. Includes data modeling, pipeline orchestration, data quality, and DataOps. Use when designing data architectures, building data pipelines, optimizing data workflows, implementing data governance, or troubleshooting data issues.
Apache Airflow workflow orchestration. Use for data pipelines.
Transform raw data into analytical assets using ETL/ELT patterns, SQL (dbt), Python (pandas/polars/PySpark), and orchestration (Airflow). Use when building data pipelines, implementing incremental models, migrating from pandas to polars, or orchestrating multi-step transformations with testing and quality checks.
Design data pipelines covering ETL vs ELT architectures, data source integration, scheduling, quality checks, and warehouse design. Use this skill when the user needs to move data between systems, build a data warehouse, automate data processing, or improve data reliability — even if they say 'move data from X to Y', 'build an ETL pipeline', 'our data is a mess', or 'set up a data warehouse'.
Build reliable data pipelines and analytics-ready datasets. USE when cleaning data, designing ETL/ELT, defining contracts, or shipping reproducible data workflows.
You are a data pipeline architecture expert specializing in scalable, reliable, and cost-effective data pipelines for batch and streaming data processing.
Transform raw data into analytical assets using ETL/ELT patterns, SQL (dbt), Python (pandas/polars/PySpark), and orchestration (Airflow). Use when building data pipelines, implementing incremental models, migrating from pandas to polars, or orchestrating multi-step transformations with testing and quality checks.
Master data engineering, ETL/ELT, data warehousing, SQL optimization, and analytics. Use when building data pipelines, designing data systems, or working with large datasets.
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.
Data pipeline expert for ETL, Apache Spark, Airflow, dbt, and data quality
You are a **Data Engineer**, an expert in designing, building, and operating the data infrastructure that powers analytics, AI, and business intelligence. You turn raw, messy data from diverse sour...
Эксперт Airbyte. Используй для настройки ETL/ELT пайплайнов, коннекторов, синхронизации данных и data pipelines.