Data is difficult to trust
Trace sources, transformation logic, tests, and metric definitions so decisions rest on evidence people can inspect.
Data engineeringServices
Start with the engineering problem, not a menu of technologies.
Dagitali provides focused data, software, and AWS engineering for organizations that need a defined problem understood, improved, and handed back in a form their team can own.
Common starting points
Recognizable problems with concrete next steps.
Analysis is hard to reproduce
Turn one-off exploration into documented methods, durable metrics, and repeatable decision-support workflows.
Data science & analyticsSoftware is expensive to change
Clarify boundaries, reduce fragile coupling, and add verification around the paths that matter most.
Software systemsDelivery depends on manual steps
Make infrastructure and releases reviewable, repeatable, and easier to operate with AWS and delivery automation.
Cloud & deliveryData foundations
Data engineering
Build clearer paths from source systems to reliable, analytics-ready data.
Useful when
- Pipelines fail silently or require repeated manual intervention
- Warehouse models are difficult to understand or test
- Data-quality expectations live only in institutional knowledge
- Scheduled workflows lack useful operating signals
Possible deliverables
- Python and SQL pipelines
- dbt Core models and schema tests
- Data contracts and validation rules
- Scheduled and monitored workflows
- Runbooks and lineage documentation
Related technical evidence: Review the ETLPlus profile
Decisions
Data science & analytics
Make analytical reasoning traceable from question and assumptions through results and limitations.
Useful when
- Teams disagree about how an important metric is defined
- An operational question needs structured exploratory analysis
- A statistical model must be explainable and reproducible
- A dashboard needs a trustworthy measurement foundation
Possible deliverables
- Exploratory and statistical analyses
- Metric and key-performance-indicator definitions
- Reproducible notebooks or analytical workflows
- Decision-support visualizations
- Assumption, limitation, and data-quality documentation
Current boundary: this service focuses on analysis, metrics, statistical modeling, and decision support rather than production machine-learning platforms.
Related technical evidence: Review the dbt Core demonstration
Applications
Software systems
Improve applications and reusable tools with maintainability, testing, and ownership in mind.
Useful when
- An inherited system needs a disciplined technical assessment
- A recurring task belongs behind an API or command-line tool
- A shared capability should become a reusable library
- Architecture decisions are slowing safe delivery
Possible deliverables
- APIs and focused internal services
- Command-line applications
- Reusable Python or Swift libraries
- Automated tests and quality checks
- Architecture records and maintenance guidance
Related technical evidence: See package design, testing, and maintenance evidence
Operations
Cloud & delivery
Use AWS infrastructure and delivery automation to make changes reviewable and releases repeatable.
Useful when
- Cloud resources are configured manually or inconsistently
- Releases depend on undocumented operator knowledge
- Credentials are long-lived or difficult to govern
- Cost, security, caching, or rollback behavior is unclear
Possible deliverables
- Python AWS CDK infrastructure
- GitHub Actions build and deployment workflows
- GitHub OIDC authentication to AWS
- Security, caching, and cost safeguards
- Deployment and operations runbooks
Related technical evidence: Review the dagitali.com infrastructure profile
Engagement fit
Useful boundaries before a conversation begins.
Fit depends on the actual problem and constraints, but these indicators help both sides avoid forcing an engagement into the wrong shape.
Likely to be a good fit
- A defined technical risk, decision, workflow, or system needs focused attention
- A responsible stakeholder can provide context, access, and timely decisions
- Documentation, verification, and transfer of ownership matter
- The work can be bounded as an assessment, implementation, or targeted support thread
Probably not the right fit
- An unlimited staff-augmentation backlog without a defined responsibility boundary
- A production machine-learning platform or round-the-clock managed operation
- A guaranteed business result that engineering work alone cannot control
- A broad transformation without an accountable decision-maker or practical starting point
Focused engagements
A bounded way to reduce uncertainty.
These are representative starting points, not fixed packages or public price commitments. Scope and outputs are confirmed before work begins.
Pipeline reliability review
Examine failure paths, data-quality controls, scheduling, observability, and operating documentation.
Analytics model review
Trace important metrics through source data, transformation logic, tests, assumptions, and presentation.
AWS delivery review
Assess infrastructure definitions, identity boundaries, deployment automation, rollback, security, and cost controls.
Maintainability review
Identify high-risk coupling, missing verification, undocumented decisions, and the smallest useful improvements.
Choose the right shape