Data and AI

Data Quality and Testing

Bring systematic, automated data quality discipline across an entire data platform, not just a single pipeline

10h4 lessonsTier 2 hireabilityTier 2 certificate

What you'll be able to do

You can design a genuine data quality framework spanning an entire pipeline lifecycle, choose appropriate data quality tooling, define meaningful data SLAs, and communicate data quality status transparently to stakeholders.

Job titles this qualifies you for

Data EngineerAnalytics EngineerData Platform Engineer
Market intelligence
Demand
High
Remote
72% remote roles
Nigeria salary
Not a primary local hiring category
Remote (USD)
$114,500–$137,500/year for the typical remote mid-level range (25th-75th percentile); average around $130,000-$148,000/year; senior remote data engineers average $191,822/year

Lessons

Stage 1 — Foundation
1
A Framework for Genuine Data Quality
30 min
2
Data Quality Tooling Beyond dbt Tests
30 min
Stage 2 — Applied
1
Defining and Measuring Data SLAs
30 min
2
Communicating Data Quality Status to Stakeholders
25 min
Tier 2 Proof Submission

Data Quality and Testing Capstone

Demonstrate genuine platform-wide data quality framework design, tooling judgement, SLA definition, and stakeholder communication.

1.A three-stage quality check plan for a customer data pipeline
2.A fixed-rule versus anomaly detection scenario
3.A specific, measurable data SLA with a missed-SLA process
4.A dashboard element list and a post-incident summary
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