Define business logic
Map source systems and define the transformation blueprint for analysis-ready datasets.
Analytics Engineering
Analytics Engineering turns raw data into structured, usable assets. We apply software-engineering rigor through testing, versioning, and modeled transformations to ensure ownership, quality, and trust at scale.
A solid data foundation
We build strong foundations through testing and quality controls. Transformations are code-based and versioned for transparency and reliability.
Data confidence
Trustworthy insights are built on trustworthy data. We implement testing across the pipeline to validate accuracy, freshness, and consistency.
Code first
We create a transparent, auditable, and collaborative workflow with peer review and rollback capabilities so data logic remains robust and maintainable.
Clear docs
We design structured data models and clear documentation so analysts and business users can find and understand the right data quickly.
Explore our solutions
Analytics engineering powers modern self-serve BI with trusted data products and actionable insights across every exposition layer.
CI/CD pipeline workflow
Design an end-to-end workflow where data moves from raw source to trusted data product in a predictable, testable, and automated way.
Trusted semantic layer
Define and govern critical metrics and dimensions as code, so each KPI has one tested, versioned, and trusted definition.
Proactive data quality
Implement multi-layer testing in the transformation pipeline to validate business logic, freshness, volume anomalies, and schema changes.
Enforced code standards
Apply clear coding standards and SQL linters to enforce consistency and maintainability across the analytics engineering codebase.
Solutions in practice
Real Estate
Powering self-serve semantic layer and conversational AI
Neho's transformation logic was scattered across siloed reporting layers. Astrafy built a governed semantic layer using dbt Cloud and Lightdash, delivering trusted data products and perfectly positioning the business for natural-language conversational analytics via Claude Desktop.
Our process
How we deliver
We treat analytics engineering like software engineering: collaborative, transparent, and built for reliability at scale.
Model iteratively
Build modular, version-controlled transformations from staging to trusted data marts.
Test and deploy
Automatically test each change for quality before deployment via CI/CD.
Document and transfer
Deliver dictionaries, lineage, and handover so your team can own the assets.
Our preferred stack
We choose tools by governance, adoption, operating model, and cost.
Google cloud
Cloud foundation for scalable, governed analytics operations.
Dataplex
Centralized catalog and governance for trusted data access.
dbt Core / dbt Cloud
SQL framework for tested models and trusted metrics.
Big Query
Warehouse for performant queries and governed access.
Looker
Governed BI and semantic modeling for consistent metrics.
Lightdash
Metrics-driven interface for fast self-serve exploration.
Datahub
Metadata and lineage visibility for stronger trust.
Analytics leaders
Move from reporting to execution while keeping metric logic stable.
Platform teams
Scale semantic governance, access control, and traceability.
Business teams
Use intuitive products and conversational access to act faster.
For teams expanding analytics access without losing metric consistency, governance, or confidence.
Who this is for
For organizations ready to democratize trusted analytics
Our resources
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