Data Engineering Project Management | Pipeline Workflows
Manage data engineering workflows for pipelines, ETL, and data quality. GitScrum provides data team labels, exploration phases, and NoteVault documentation.
4 min read
Data engineering involves pipelines, transformations, and quality initiatives with long feedback loops. GitScrum supports data teams with workflow tracking for ETL development, data quality labels, and visibility into the multi-stage nature of data projects.
Data Engineering Patterns
Unique Workflow Needs
DATA ENGINEERING CHALLENGES:
┌─────────────────────────────────────────────────────────────┐
│ DATA TEAM WORK PATTERNS │
├─────────────────────────────────────────────────────────────┤
│ │
│ PIPELINE DEVELOPMENT: │
│ ┌─────────────────────────────────────────────────────────┐│
│ │ • ETL/ELT pipeline creation ││
│ │ • Data source integrations ││
│ │ • Transformation logic ││
│ │ • Scheduling and orchestration ││
│ └─────────────────────────────────────────────────────────┘│
│ │
│ DATA QUALITY: │
│ ┌─────────────────────────────────────────────────────────┐│
│ │ • Data validation rules ││
│ │ • Quality monitoring ││
│ │ • Issue investigation ││
│ │ • Remediation and cleanup ││
│ └─────────────────────────────────────────────────────────┘│
│ │
│ STAKEHOLDER REQUESTS: │
│ ┌─────────────────────────────────────────────────────────┐│
│ │ • Analytics team data needs ││
│ │ • ML team feature requests ││
│ │ • Business reporting requirements ││
│ │ • Ad-hoc data extracts ││
│ └─────────────────────────────────────────────────────────┘│
│ │
│ ITERATION PATTERNS: │
│ ┌─────────────────────────────────────────────────────────┐│
│ │ • Long feedback loops (wait for data) ││
│ │ • Exploratory work before building ││
│ │ • Schema evolution ││
│ │ • Backfill requirements ││
│ └─────────────────────────────────────────────────────────┘│
│ │
└─────────────────────────────────────────────────────────────┘
Board Structure
Data Engineering Columns
| Column | Purpose |
|---|---|
| Requests | Incoming data needs |
| Exploration | Data discovery |
| Design | Pipeline architecture |
| Development | Building pipeline |
| Testing | Data validation |
| Staging | Pre-prod run |
| Production | Live pipeline |
| Monitoring | Ongoing health |
Label System
Data Team Labels
DATA ENGINEERING LABELS:
┌─────────────────────────────────────────────────────────────┐
│ ORGANIZING DATA WORK │
├─────────────────────────────────────────────────────────────┤
│ │
│ TYPE LABELS: │
│ ┌─────────────────────────────────────────────────────────┐│
│ │ type:pipeline ││
│ │ type:data-quality ││
│ │ type:integration ││
│ │ type:transformation ││
│ │ type:ad-hoc ││
│ │ type:documentation ││
│ └─────────────────────────────────────────────────────────┘│
│ │
│ SOURCE LABELS: │
│ ┌─────────────────────────────────────────────────────────┐│
│ │ source:postgres ││
│ │ source:api ││
│ │ source:s3 ││
│ │ source:salesforce ││
│ │ source:events ││
│ └─────────────────────────────────────────────────────────┘│
│ │
│ DESTINATION LABELS: │
│ ┌─────────────────────────────────────────────────────────┐│
│ │ dest:warehouse ││
│ │ dest:datalake ││
│ │ dest:analytics ││
│ │ dest:ml-features ││
│ └─────────────────────────────────────────────────────────┘│
│ │
│ STAKEHOLDER: │
│ ┌─────────────────────────────────────────────────────────┐│
│ │ team:analytics ││
│ │ team:ml ││
│ │ team:business-ops ││
│ │ team:finance ││
│ └─────────────────────────────────────────────────────────┘│
│ │
└─────────────────────────────────────────────────────────────┘
Pipeline Development
Task Breakdown
PIPELINE TASK STRUCTURE:
┌─────────────────────────────────────────────────────────────┐
│ PIPELINE PROJECT TASKS │
├─────────────────────────────────────────────────────────────┤
│ │
│ EXPLORATION PHASE: │
│ ┌─────────────────────────────────────────────────────────┐│
│ │ • Explore source data schema ││
│ │ • Document data quality issues ││
│ │ • Identify transformation needs ││
│ │ • Estimate volume and frequency ││
│ └─────────────────────────────────────────────────────────┘│
│ │
│ DEVELOPMENT TASKS: │
│ ┌─────────────────────────────────────────────────────────┐│
│ │ • Extract job implementation ││
│ │ • Transform logic ││
│ │ • Load to destination ││
│ │ • Data quality checks ││
│ │ • Scheduling configuration ││
│ └─────────────────────────────────────────────────────────┘│
│ │
│ VALIDATION TASKS: │
│ ┌─────────────────────────────────────────────────────────┐│
│ │ • Test with sample data ││
│ │ • Validate transformations ││
│ │ • Performance testing ││
│ │ • Edge case handling ││
│ └─────────────────────────────────────────────────────────┘│
│ │
│ DEPLOYMENT: │
│ ┌─────────────────────────────────────────────────────────┐│
│ │ • Deploy to staging ││
│ │ • Run backfill if needed ││
│ │ • Deploy to production ││
│ │ • Set up monitoring ││
│ └─────────────────────────────────────────────────────────┘│
│ │
└─────────────────────────────────────────────────────────────┘
Documentation
NoteVault for Data Teams
| Note | Content |
|---|---|
| Data catalog | Available datasets |
| Pipeline docs | Architecture and logic |
| Quality rules | Validation definitions |
| Runbooks | Operational procedures |