Data engineering
Reliable pipelines, thoughtful data models, analytics foundations, and tooling that make information useful.
- Python & SQL pipelines
- dbt Core warehouse models
- Data quality checks
- Scheduled & monitored workflows
Data engineering · Data science · Software systems
Turn complex data into systems your team can trust.
Dagitali designs reliable pipelines, analytical foundations, and software that help teams move from uncertain information to confident action.
What we do
Technical depth, translated into useful business capability.
From the first source system to the decision it supports, each layer is designed to be understandable, testable, and ready for change. Dagitali works with small and growing organizations and established teams that need focused data, software, or AWS expertise for a defined problem.
Explore services and focused engagementsData science & analytics
Exploratory data analysis, metric design, statistical modeling, and reproducible decision support grounded in traceable data. The focus is analysis and decision support rather than production machine-learning platforms.
- Exploratory data analysis
- Statistical modeling
- Metrics, key performance indicators & dashboards
- Reproducible decision-support workflows
Software systems
Well-structured applications and libraries designed for maintainability, testing, and long-term ownership.
- APIs & services
- Command-line tools
- Reusable Swift & Python libraries
- Architecture
Cloud & delivery
Practical AWS infrastructure and build/delivery automation that help teams ship with confidence and reduce operational friction.
- Infrastructure as code
- CI/CD pipelines
- Operational documentation
Outcomes we design for
More confidence in every next move.
Dagitali designs foundations intended to make decisions easier, delivery steadier, and future changes less risky.
Traceable decisions
Data people can follow from its source to the metric, model, or action it informs.
Calmer delivery
Automated checks and repeatable workflows replace fragile, manual release habits.
Confident ownership
Documentation and knowledge transfer help the team operate and extend what was built.
A practical fit
Focused engineering works best with a real boundary.
Dagitali is most useful when a defined technical risk, decision, workflow, or system needs focused attention—and a responsible stakeholder can provide context and timely decisions.
Defined problemThe outcome or uncertainty can be stated clearly enough to investigate.
Inspectable workVerification, documentation, and consequential decisions stay visible.
Practical ownershipThe client intends to operate and extend the result after handoff.
Selected engineering work
Engineering proof lives in the work.
Public repositories and this website’s production architecture show how Dagitali approaches reusable engineering, documentation, testing, and maintainable systems. Read the technical profiles
ETLPlusReusable packageReusable ETL operations through a tested Python package and command-line interface
dbt Demo: Jaffle ShopStarter demonstrationA dbt Core project demonstrating SQL models, ref-based dependencies, schema tests, and local checks
dagitali.comProduction infrastructure examplePrivate S3 and CloudFront hosting defined with Python AWS CDK and deployed through GitHub OIDC
Engineering insights
Useful questions before tools become commitments.
Dagitali publishes practical guidance grounded in its own engineering work, with evidence and limitations kept visible.
Data engineering
What makes a data pipeline operable?
Look beyond transformation logic to expectations, failure visibility, recovery behavior, and operating knowledge.
Read the insightAnalytics engineering
What should a useful dbt model review examine?
Connect model purpose and grain to dependencies, tests, documentation, materialization, and ownership.
Read the insightCloud & delivery
Why private S3, CloudFront, and GitHub OIDC?
Keep a static site’s origin private, delivery repeatable, caching explicit, and deployment credentials short-lived.
Read the insightHow we work
Consistent senior engineering attention from question to handoff.
Engagements can begin with a focused assessment, a defined implementation, or targeted engineering support for an existing team. Senior-level engineering judgment guides discovery, implementation decisions, technical review, and handoff.
Compare engagement modelsFrame the real problem
Clarify the decision, constraints, users, and definition of done before adding machinery.
Build the smallest sound solution
Favor understandable architecture, automated checks, and incremental delivery that reduces ambiguity.
Leave the system stronger
Hand off source code and, where applicable, infrastructure definitions, automated checks, operating instructions, and recorded design decisions so the next change is easier than the last.
Ready to move forward?
Bring the hard part. Let’s make the next step concrete.
Start with a short conversation to clarify the problem, important constraints, and whether Dagitali is a practical fit.
Start a conversation