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Found 56 Skills
Optimize BigQuery compute costs by assigning data models (Dataform, dbt, Airflow) to slot reservations or on-demand compute based on Masthead recommendations.
Build scalable data pipelines, modern data warehouses, and real-time streaming architectures. Implements Apache Spark, dbt, Airflow, and cloud-native data platforms. Use PROACTIVELY for data pipeline design, analytics infrastructure, or modern data stack implementation.
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.
Strategic guidance for operationalizing machine learning models from experimentation to production. Covers experiment tracking (MLflow, Weights & Biases), model registry and versioning, feature stores (Feast, Tecton), model serving patterns (Seldon, KServe, BentoML), ML pipeline orchestration (Kubeflow, Airflow), and model monitoring (drift detection, observability). Use when designing ML infrastructure, selecting MLOps platforms, implementing continuous training pipelines, or establishing model governance.
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, batch processing, data quality. NOT for: API design (use api-architect), ML training (use ML skills), dashboards (use design skills).
Create and adapt Bolt Pipeliner ETL projects using Spark, Pandas, or Polars. Use whenever a user asks to create an ETL pipeline, data lake pipeline, medallion architecture, Airflow pipeline, data-quality workflow, or Bolt Pipeliner project.
This skill should be used when the user designs a "task scheduler", "job scheduler", "job queue", "cron at scale", "distributed cron", "delayed / scheduled / recurring tasks", a "worker pool", reaches for "Celery / Sidekiq / Airflow", or wrestles with "task leasing", visibility timeouts, job priorities, fairness, or duplicate task execution. Use it whenever work must run later, on a schedule, or be reliably leased to a pool of workers, even if the user doesn't say "scheduler". (For plain queue transport and delivery guarantees, that is `messaging-streaming`.)
Astronomer integration. Manage data, records, and automate workflows. Use when the user wants to interact with Astronomer data.
Quick data freshness check. Use when the user asks if data is up to date, when a table was last updated, if data is stale, or needs to verify data currency before using it.
Comprehensive DAG failure diagnosis and root cause analysis. Use for complex debugging requests requiring deep investigation like "diagnose and fix the pipeline" or "full root cause analysis".
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 ETL/ELT pipelines with proper orchestration, error handling, and monitoring. Use when building data pipelines, designing data workflows, or implementing data transformations.