Etl Workflow Designer

Overview

Produces a complete, professional ETL/ELT workflow design document and implementation plan for a specific data movement scenario. Includes source profiling questions, connector selection guide, transformation catalog, scheduling strategy, error handling matrix, monitoring, and a design document template with Mermaid diagrams that can be turned into working code.

When to Use This Skill

• Planning a new data integration or migration project.
• The user describes "move data from A to B" or "build a pipeline that...".
• You need to create a design document before coding (good for alignment with stakeholders).

Prerequisites

• Clear source(s) and destination(s).
• Rough understanding of data volume, freshness requirements, and SLAs.
• Access to documentation or sample data from the source system.

Steps

1. Source profiling (ask these questions and document answers):

   • What is the source system and access method (API, DB, files, stream)?
   • Volume (rows/day, GB)?
   • Update pattern (append-only, CDC, full refresh)?
   • Schema stability?
   • Authentication / rate limits?

2. Destination profiling:

   • Warehouse, lake, app DB, search index, etc.
   • Required freshness (real-time, hourly, daily)?
   • Query patterns (what will consume the data)?

3. Connector & tool selection:

   • Batch: Airflow + Python, dbt, Fivetran, Stitch, custom scripts.
   • Streaming: Kafka, Kinesis, Debezium CDC, Spark Structured Streaming.
   • File: AWS Glue, Azure Data Factory, custom.

4. Transformation catalog:

   • Filter, map/rename, join/enrich, aggregate, type cast, deduplicate, validate, mask/PII.
   • Document each transform with input → output example.

5. Error handling matrix:

   • Transient (retry with backoff).
   • Data quality (quarantine + alert).
   • Schema drift (alert + fallback).
   • Permanent failure (DLQ + manual review).

6. Scheduling & orchestration:

   • Cron, event-driven, dependency-based (Airflow DAG).
   • Watermark / incremental logic.
   • Backfill strategy.

7. Monitoring & SLAs:

   • Freshness, volume, latency, data quality score.
   • Alerting channels and escalation.

8. Output:

   • Design document (Markdown) with:
     • Context & requirements
     • Architecture diagram (Mermaid)
     • Data flow
     • Transformation list
     • Error handling table
     • Implementation phases & timeline
     • Risks & open questions
   • High-level code skeleton or Airflow DAG outline.
   • Next-step recommendations (prototype the extract first).

Examples

A complete design document for "Ingest Shopify orders + customers into Snowflake daily, with incremental logic, PII masking, and quality checks" including Mermaid architecture diagram, transformation catalog, error matrix, and 4-week implementation plan is included.

Edge Cases & Error Handling

• Source downtime: Backpressure, queueing, or graceful degradation.
• Schema evolution: Versioned schemas or automated drift detection.
• Compliance (GDPR, HIPAA): Document data lineage and masking strategy.

Verification

1. The design document is reviewed and approved by stakeholders (data + engineering + business).
2. A small prototype of the critical path (extract → minimal transform → load) succeeds.
3. The Mermaid diagram renders and accurately reflects the planned flow.
4. Error scenarios are covered in the matrix with clear ownership.
5. Success: The team has a clear, shared plan that can be implemented with minimal rework.

References

• Data Engineering Lifecycle
• Airflow Best Practices
• dbt Best Practices
• Mermaid Diagrams