Data Engineering Projects | Pipeline Tracking

Data engineering projects need pipeline-specific labels and ETL tracking. GitScrum documents data models in NoteVault and delivers 40% more reliable pipelines.

4 min read

How to use GitScrum for data engineering projects?

Manage data engineering in GitScrum with labels for pipeline types, track ETL development through standard workflow, and document data models in NoteVault. Coordinate with analysts on requirements, track data quality issues, and manage infrastructure work. Organized data teams deliver 40% more reliable pipelines [Source: Data Engineering Research 2024].

Data engineering workflow:

Requirements - From analysts/stakeholders

Design - Data model, pipeline design

Develop - Build ETL/pipeline

Test - Data validation

Stage - Pre-production testing

Deploy - Production release

Monitor - Ongoing quality

Data engineering labels

Label	Purpose
type-etl	ETL pipelines
type-streaming	Real-time pipelines
type-analytics	Analytics models
type-infrastructure	Data infrastructure
data-quality	Quality issues
source-[name]	Data source
destination-[name]	Data destination

Pipeline task template

## Pipeline: [name]

### Details
- Source: [system/table]
- Destination: [warehouse/table]
- Schedule: [cron/trigger]
- SLA: [freshness requirement]

### Checklist
- [ ] Schema design
- [ ] Transform logic
- [ ] Data validation
- [ ] Performance test
- [ ] Deploy to staging
- [ ] Stakeholder review
- [ ] Deploy to production
- [ ] Monitor setup

NoteVault data documentation

Document	Content
Data catalog	Tables, columns, types
Data lineage	Source to destination
Pipeline inventory	All pipelines
Data dictionary	Business definitions
SLAs	Freshness requirements

Columns for data projects

Column	Purpose
Backlog	All work
Design	Schema, logic design
Development	Building
Testing	Data validation
Staging	Pre-production
Production	Deployed

Data quality tracking

Issue Type	Label
Missing data	dq-completeness
Wrong data	dq-accuracy
Late data	dq-timeliness
Duplicate data	dq-uniqueness
Format issues	dq-consistency

Stakeholder coordination

Stakeholder	Coordination
Analysts	Requirements, validation
Business	SLA definition
Engineering	Source system access
Security	Data access controls

Pipeline dependencies

Dependency	Handling
Source changes	Linked tasks
Upstream pipelines	Run order
Schema changes	Migration tasks
Infrastructure	Platform tasks

Data testing checklist

Test	Verify
Row counts	Expected volume
Null checks	Required fields
Range checks	Valid values
Referential	Key relationships
Freshness	SLA compliance

Common data engineering issues

Issue	Solution
Undocumented pipelines	NoteVault requirement
Quality issues	Monitoring tasks
Long runs	Performance tasks
Failed jobs	Incident workflow

Data team metrics

Metric	Track
Pipeline reliability	% successful runs
Data freshness	SLA compliance
Quality issues	Open DQ tasks
Delivery time	Task cycle time