SQL
Use a project where SQL changes a real implementation decision. Show the code, artifact, test, metric or operational evidence behind the claim.
CAREER PATH · Data Analyst / Engineer
Turn raw data into decisions and reproducible systems.
Demonstrate SQL, data quality, analysis, visualization and communication—not screenshots alone.
SQLPythonData modelingVisualizationQuality checksCommunication
WHAT TO PROVE
Turn role requirements into visible evidence.
A portfolio is stronger when each project makes one or two important capabilities easy to inspect instead of trying to mention every technology at once.
Python
Use a project where Python changes a real implementation decision. Show the code, artifact, test, metric or operational evidence behind the claim.
Data modeling
Use a project where Data modeling changes a real implementation decision. Show the code, artifact, test, metric or operational evidence behind the claim.
Visualization
Use a project where Visualization changes a real implementation decision. Show the code, artifact, test, metric or operational evidence behind the claim.
Quality checks
Use a project where Quality checks changes a real implementation decision. Show the code, artifact, test, metric or operational evidence behind the claim.
Communication
Use a project where Communication changes a real implementation decision. Show the code, artifact, test, metric or operational evidence behind the claim.
RECOMMENDED PROJECTS
6 projects for the Data Analyst / Engineer path.
Choose one project you can finish, deploy and explain deeply. Quality of proof matters more than collecting unfinished repositories.
Beginner–Intermediate1 weekend
Product Funnel Analysis
Turn messy event data into reproducible funnel analysis with SQL, quality checks and a decision memo.
SQLPythonData cleaningVisualizationCommunication
Open project brief →Intermediate2 weekends
Analytics Warehouse with dbt
Model raw product and revenue data into tested analytics tables with documentation and lineage.
SQLdbtData modelingTestingDocumentation
Open project brief →Intermediate1–2 weekends
A/B Experiment Analysis Toolkit
Analyze experiments with guardrails, confidence intervals, segmentation and clear decision rules.
StatisticsPythonSQLExperimentsCommunication
Open project brief →Advanced2 weekends
Data Quality Monitoring Pipeline
Detect freshness, schema and distribution problems before broken data reaches dashboards.
Data qualityPythonSQLMonitoringPipelines
Open project brief →Intermediate2 weekends
Customer Churn Analysis + Model
Build interpretable churn analysis from raw data through validation and stakeholder recommendations.
PythonSQLModelingFeature engineeringCommunication
Open project brief →Advanced2–3 weekends
Streaming Events Analytics
Process event streams into near-real-time aggregates and expose freshness and lag metrics.
StreamingSQLPipelinesDashboardsObservability
Open project brief →RELEVANT OFFICIAL CERTIFICATIONS
Credentials that can complement the project proof.
These are optional signals from official issuers, not requirements for the role. When a pasted job explicitly mentions a credential, SkillChirp can surface it separately from project skill coverage.
AWSAssociate
AWS Certified Data Engineer – Associate
AWS data credential centered on data models, ingestion and transformation, lifecycle management and data quality.
AWSData pipelinesData modelingData qualitySecurity
MicrosoftFoundational
Microsoft Certified: Azure AI Fundamentals
Beginner AI credential covering AI concepts and implementing AI solutions with Microsoft Azure tooling.
AzureAIMachine learningGenerative AIPython
MicrosoftFoundational
Microsoft Certified: Azure Data Fundamentals
Foundational credential for relational and non-relational data concepts and Azure data workloads.
AzureDataDatabasesAnalyticsData workloads
Google CloudProfessional
Google Cloud Professional Data Engineer
Professional data credential for designing, building, operationalizing and securing data-processing systems.
Google CloudData engineeringData pipelinesData qualityAnalytics
Google CloudProfessional
Google Cloud Professional Machine Learning Engineer
Professional ML credential for building, productionizing and monitoring machine-learning and generative-AI solutions.
Google CloudMachine learningMLOpsGenerative AIMonitoring
HOW TO USE THESE PROJECTS
Build, document, then make the proof easy to review.
Pick one missing signal
Choose the project that best fills a gap in your current portfolio rather than the one with the longest technology list.
Ship a working core
Keep scope small enough to finish. A deployed, testable core gives you more evidence than an ambitious unfinished system.
Document the tradeoffs
Explain architecture, constraints, failure states, testing and what you would change with more time.
Validate the work
Use SkillChirp discovery and Battles where appropriate to expose the project to outside feedback instead of leaving it hidden in a repository.
Make the project visible.
After you ship it, use the rest of SkillChirp to get discovery, validation and feedback instead of leaving the work hidden in a repository.