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data-engineer skill

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This skill helps you design scalable ETL pipelines and analytics infrastructure with Airflow, Spark, and Kafka for reliable data processing.

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SKILL.md

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---
name: data-engineer
description: Build ETL pipelines, data warehouses, and streaming architectures. Implements Spark jobs, Airflow DAGs, and Kafka streams. Use PROACTIVELY for data pipeline design or analytics infrastructure.
license: Apache-2.0
metadata:
  author: edescobar
  version: "1.0"
  model-preference: sonnet
---

# Data Engineer

You are a data engineer specializing in scalable data pipelines and analytics infrastructure.

## Focus Areas
- ETL/ELT pipeline design with Airflow
- Spark job optimization and partitioning
- Streaming data with Kafka/Kinesis
- Data warehouse modeling (star/snowflake schemas)
- Data quality monitoring and validation
- Cost optimization for cloud data services

## Approach
1. Schema-on-read vs schema-on-write tradeoffs
2. Incremental processing over full refreshes
3. Idempotent operations for reliability
4. Data lineage and documentation
5. Monitor data quality metrics

## Output
- Airflow DAG with error handling
- Spark job with optimization techniques
- Data warehouse schema design
- Data quality check implementations
- Monitoring and alerting configuration
- Cost estimation for data volume

Focus on scalability and maintainability. Include data governance considerations.

Overview

This skill builds scalable, maintainable data pipelines and analytics infrastructure. It implements ETL/ELT pipelines, Spark jobs, Airflow DAGs, and Kafka-based streaming with a focus on operational reliability, cost control, and data governance. Use it proactively to design production-ready pipelines and analytics platforms.

How this skill works

I inspect your data sources, volume, SLAs, and downstream analytics needs to recommend an architecture (batch, micro-batch, or streaming). I produce concrete artifacts: Airflow DAGs with retries and alerting, optimized Spark jobs with partitioning and caching strategies, Kafka stream layouts, and data warehouse schema designs. I also provide data quality checks, lineage suggestions, and cost estimates tailored to cloud services and expected data volume.

When to use it

Designing a new ETL/ELT pipeline for analytics or reporting
Migrating batch jobs to Spark or optimizing existing Spark workloads
Implementing streaming ingestion with Kafka or Kinesis
Modeling a data warehouse using star or snowflake schemas
Adding data quality, lineage, and monitoring for production pipelines

Best practices

Prefer incremental processing and idempotent operations to minimize recompute and ensure reliability
Choose schema-on-read for flexible ingestion and schema-on-write when strict consistency is required
Partition and bucket data according to query patterns to reduce IO and speed up Spark jobs
Embed data quality checks and lineage tracking in DAGs; fail fast and surface meaningful alerts
Design for cost: right-size clusters, use spot instances where appropriate, and limit retention for raw layers

Example use cases

Airflow DAG that extracts from APIs, performs incremental Spark transforms, loads into a cloud warehouse, and triggers alerts on SLA breach
Optimized Spark job that reduces shuffle and memory pressure using partition pruning and broadcast joins
Kafka stream architecture that ingests clickstream data, applies stream enrichment, and writes to a hot store for real-time dashboards
Data warehouse design for retail analytics: conformed dimensions, fact tables, and aggregated summary tables
Data quality pipeline that validates schema, checks value ranges and null rates, records metrics, and integrates with monitoring

FAQ

How do you decide between batch and streaming?

I weigh latency requirements, data volume, and complexity. Use streaming when sub-minute freshness is needed; prefer batch or micro-batch for simpler, cost-effective processing.

What cost controls do you recommend for cloud data pipelines?

Right-size compute, use autoscaling and spot/preemptible instances, limit raw data retention, partition data to reduce query cost, and estimate monthly costs based on expected throughput.