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SkillsMP ha recopilado 25 skills de Methasit-Pun/data_engineer_claude_skills. Abre una skill para revisar su origen y sus detalles.
Mostrando 25 de 25 skills recopiladas.
Umbrella skill for running a data project end-to-end through its lifecycle stages — discover sources → profile the data → architect the platform → build the medallion pipeline → refactor the code. Use this whenever the user is kicking off a new data project,…
Idioma del texto original: inglés
Data collection and readiness — the step BEFORE a pipeline exists. Catalog candidate data sources for an objective, rank each by importance/impact, and record where to get it (internal system vs. open/public dataset), how to access it, its refresh cadence,…
Idioma del texto original: inglés
Profile and map raw data BEFORE designing a schema. One-time exploratory analysis to learn the true shape of a dataset — row/column counts, null rates, cardinality, value distributions, ranges, data types, candidate keys, duplicates, referential relationships…
Idioma del texto original: inglés
Umbrella skill for shaping data once it has landed — warehouse schema design (star/snowflake/OBT/SCD/grain), SQL for analytics (window functions, CTEs, optimization), dbt model layers/tests/macros/incrementals, and Python transforms (pandas/Polars/PySpark…
Idioma del texto original: inglés
dbt model design, ref chains, sources, tests, macros, incremental strategies, materializations, and documentation best practices. Use this skill whenever the user is writing or reviewing dbt models, configuring dbt tests, designing model layers…
Idioma del texto original: inglés
Pandas, Polars, and PySpark idioms for production data engineering — chunked reads, memory-safe transforms, vectorized operations, type optimization, and performance patterns. Use this skill whenever the user is writing a Python data transformation script and…
Idioma del texto original: inglés
Data modeling for analytical workloads — star schema, snowflake schema, one big table (OBT), slowly changing dimensions (SCD), normalization tradeoffs, grain definition, and surrogate key strategies. Use this skill whenever the user is designing or reviewing…
Idioma del texto original: inglés
Best-practice SQL for analytical workloads — window functions, CTEs, query optimization, partitioning strategies, and anti-patterns to avoid. Use this skill whenever the user is writing or reviewing a SQL query that goes beyond a basic SELECT, especially on…
Idioma del texto original: inglés
Umbrella skill for where data pipelines run and what they cost — AWS/GCP/Azure infrastructure (S3/GCS/ADLS layout, BigQuery/Redshift/Snowflake selection, IAM, managed-service choice, performance tuning) and cost control (bytes scanned, partition pruning,…
Idioma del texto original: inglés
AWS/GCP/Azure data infrastructure — S3/GCS/ADLS partitioning, BigQuery slot management, Redshift spectrum, Snowflake warehouses, IAM roles for data access, cost optimization, and managed service selection. Use this skill whenever the user is deploying a…
Idioma del texto original: inglés
Query cost analysis, partition pruning, slot reservation strategies, storage tiering, and cloud data warehouse cost reduction. Use this skill whenever the cloud data bill is unexpectedly high, a specific query is scanning too much data, the team wants to…
Idioma del texto original: inglés
Objective-first data architecture design and decision framework. Establish the target object FIRST (BI, ML, or both), elicit requirements and constraints (cloud vs. on-prem, resource/compute budget, team skills, latency and freshness SLAs, compliance), then…
Idioma del texto original: inglés
Moving data between systems safely — cutover planning, backfill strategies, dual-write patterns, validation, rollback procedures, and zero-downtime migration techniques. Use this skill whenever the team is migrating from one database or warehouse to another…
Idioma del texto original: inglés
Umbrella skill for moving data from source to destination — end-to-end ETL/ELT design, DAG orchestration, real-time streaming, and system-to-system migration. Use this whenever the user is building, scheduling, debugging, or migrating a pipeline and it isn't…
Idioma del texto original: inglés
Design a medallion (bronze/silver/gold) ETL architecture interactively, objective-first. List the available data, confirm the objective, then design GOLD first to match the objective and get the user to review it before moving down to silver, then bronze…
Idioma del texto original: inglés
Airflow/Prefect/Dagster DAG design — task dependencies, retries, SLAs, backfill strategies, sensors, and failure recovery. Use this skill whenever the user is building or debugging a scheduled pipeline with multiple steps, asking how to handle task failures,…
Idioma del texto original: inglés
Design ETL/ELT pipelines end-to-end — source connectors, extraction strategies, transform logic, load patterns, idempotency, scheduling, and error handling. Use this skill whenever the user is starting a new ingestion job, planning how data moves from a…
Idioma del texto original: inglés
Kafka, Flink, Kinesis, and Spark Structured Streaming design — consumer groups, partitioning, exactly-once semantics, lag monitoring, windowing, and late-arriving data. Use this skill whenever the user needs real-time or near-real-time data processing, is…
Idioma del texto original: inglés
Define and enforce schema contracts between producer and consumer teams — field types, nullability, allowed values, versioning, breaking vs. non-breaking changes, and change detection patterns. Use this skill whenever two teams or services share a dataset and…
Idioma del texto original: inglés
Data lineage tracking, PII tagging, access control policies, data catalog metadata standards, retention policies, and audit logging for regulatory compliance. Use this skill whenever the company is subject to PDPA, GDPR, HIPAA, or any data privacy regulation,…
Idioma del texto original: inglés
Write systematic data quality checks — validation rules, Great Expectations suites, dbt tests, anomaly detection, null/type/range/referential integrity assertions, and monitoring patterns for production pipelines. Use this skill whenever the user is dealing…
Idioma del texto original: inglés
Umbrella skill for making data correct, trustworthy, and compliant — validation and quality checks (Great Expectations, dbt tests, anomaly/null/range/referential assertions), producer↔consumer schema contracts (versioning, breaking-change detection), and…
Idioma del texto original: inglés
Feature store patterns, training/serving skew prevention, feature pipelines for ML teams, point-in-time correct joins, and bridging data engineering with MLOps conventions. Use this skill whenever an ML team needs feature pipelines, when building a feature…
Idioma del texto original: inglés
Refactor a Jupyter/.ipynb notebook from top-to-bottom exploratory sprawl into small, named, reviewable functions with clear inputs and outputs. Extract subfunctions, separate config/params from logic, remove hidden cell-order dependencies and global-state…
Idioma del texto original: inglés
Translate pipeline metrics, SLA breaches, data quality failures, and incidents into clear non-technical summaries for business stakeholders. Use this skill whenever data was late, wrong, or missing and someone needs to communicate what happened to a manager,…
Idioma del texto original: inglés