Data teams formalising transformation work
Move SQL, notebooks, and report logic into versioned, reviewed, tested, documented, and deployable projects.
Versioned transformations, data tests, documentation, lineage, semantic models, and governed metrics
Analytics engineering
Rokad applies software-engineering discipline to analytical data through versioned transformations, testing, documentation, lineage, deployment, and semantic modelling.
Designed for / 01
A focused delivery model for the organisations that need it.
Analytics engineering turns raw warehouse data into reliable, reusable business models. Rokad structures transformation projects, source models, staging, intermediate logic, marts, tests, documentation, lineage, metric definitions, CI/CD, and production operations for analytical data.
Organisations standardising metrics
Create reusable models and semantic definitions used consistently across dashboards, analysis, applications, and AI.
Companies scaling analyst self-service
Provide trusted data products that reduce repeated joins, cleaning, and metric reconstruction by every consumer.
Challenges / 02
The problems this service is built to solve.
Business logic is hidden inside dashboards
Important joins, filters, calculations, and exclusions are duplicated and difficult to test or reuse.
Transformation changes reach production without evidence
SQL is not reviewed, tested, documented, compared, or deployed through controlled environments and releases.
Model dependencies are difficult to understand
Teams cannot see source lineage, downstream impact, owners, freshness, usage, or the reason a model exists.
Capabilities / 03
What Rokad can deliver.
01
Transformation-project architecture, conventions, layers, and ownership
02
Source, staging, intermediate, mart, domain, and semantic modelling
03
SQL, code, macros, reusable packages, incremental models, and snapshots
04
Schema, uniqueness, relationship, accepted-value, business, and reconciliation tests
05
Documentation, lineage, exposures, ownership, contracts, and data catalogue integration
06
Pull requests, CI, environments, deployment, state comparison, and rollback
07
Metric definitions, semantic layers, observability, performance, and managed operation
Platform expertise
Platform-specific implementation services.
Solution components / 04
The system behind the visible product.
Transformation architecture
Project layout, layers, naming, grain, materialisation, dependencies, reuse, ownership, and lifecycle.
Quality system
Contracts, tests, source freshness, reconciliation, anomaly detection, review, and failure response.
Delivery system
Version control, pull requests, CI, environments, state comparison, deployment, documentation, and release evidence.
Semantic products
Certified marts, metrics, dimensions, entities, documentation, permissions, and interfaces for BI and analysis.
Use cases / 05
Where this capability creates practical leverage.
dbt or transformation-framework implementation
Establish repositories, conventions, layers, tests, CI/CD, documentation, environments, and team workflows.
Metric standardisation
Move duplicated calculations into governed semantic models with clear grain, dimensions, ownership, and tests.
Dashboard-logic migration
Extract joins, cleaning, calculations, and business rules from BI tools into reusable warehouse models.
Analytics model modernisation
Restructure slow, opaque, tightly coupled transformations into layered, documented, tested, and incremental data products.
Architecture and integration / 06
Designed to fit the wider technology environment.
One grain per model
Define what each row represents, keys, time, dimensions, measures, history, and ownership before implementation.
Thin semantic interfaces
Expose stable business entities and metrics while keeping source complexity and transformation detail behind documented models.
Change impact before merge
Use lineage, contracts, tests, state comparison, sampling, and downstream ownership to evaluate transformation changes.
Quality and control / 07
Production requirements are part of the build.
Trust through contracts
Ownership, schemas, semantics, freshness, completeness, access, and failure expectations are explicit between producers and consumers.
Tested transformation
Pipelines and models include validation, reconciliation, lineage, observability, and controlled change before decision use.
Governed access
Identity, classification, least privilege, retention, masking, audit, and usage boundaries follow the sensitivity of the data.
Delivery / 08
A controlled path from requirement to operation.
Discover
Clarify the objective, users, systems, constraints, dependencies, risks, and measurable acceptance criteria.
Architect
Define the target design, interfaces, controls, migration or delivery sequence, and operating model.
Deliver and validate
Implement in controlled increments with testing, review, documentation, observability, and stakeholder validation.
Operate and improve
Establish ownership, service controls, measurement, support, and a prioritised improvement backlog.
Typical deliverables
Analytics transformation, metric, quality, and workflow assessment
Project architecture, modelling conventions, ownership, and roadmap
Production source, staging, intermediate, mart, and semantic models
Tests, contracts, reconciliation, documentation, and lineage
CI/CD, environments, deployment, review, and release workflows
Metric catalogue, runbooks, contribution, and operating documentation
Engagement models / 09
Use the delivery structure that matches the work.
Assessment and roadmap
A bounded evidence review, target direction, prioritised risks, and executable next-stage plan.
Fixed-scope delivery
A defined implementation, migration, prototype, procurement, or transformation outcome with acceptance criteria.
Embedded specialists
Specialists working alongside internal product, engineering, data, operations, security, or procurement teams.
Managed lifecycle
Ongoing ownership, maintenance, monitoring, supplier coordination, reliability, security, and improvement.
Related capabilities / 10
Continue through the wider product and technology system.
01
Business intelligence
Deliver dashboards and reporting on governed analytical models.
02
Data warehousing
Provide historical storage, performance, and integrated business models.
03
Data pipelines
Deliver reliable source data into transformation projects.
01
AI development
Governed AI applications, agents, retrieval, models, evaluation, and intelligent automation.
02
Software development
Custom applications, platforms, integrations, APIs, and software modernisation.
03
Managed technology services
Application, cloud, security, reliability, maintenance, and continuous engineering operations.
FAQ
Analytics engineering
Scope, ownership, assumptions, delivery, security, and long-term operation are clarified before work begins.
01Is analytics engineering only about dbt?
Is analytics engineering only about dbt?
No. dbt is one useful tool. The discipline includes modelling, tests, contracts, version control, review, documentation, lineage, deployment, semantics, and ownership regardless of platform.
02Can Rokad improve an existing dbt project?
Can Rokad improve an existing dbt project?
Yes. We can review layers, naming, grain, tests, materialisations, incremental logic, performance, packages, CI/CD, documentation, ownership, and metric consistency.
03How much logic should remain in BI tools?
How much logic should remain in BI tools?
Presentation-specific calculations may remain there, but reusable cleaning, joins, business rules, entities, dimensions, and governed metrics should usually live in tested data models.
04Can analytics models support AI and applications?
Can analytics models support AI and applications?
Yes. Stable, tested models can serve features, retrieval, APIs, exports, applications, and evaluation, though operational latency and contracts may require additional serving layers.
Data engineering
Treat analytical data as production software with users and contracts.
Rokad can structure the models, tests, documentation, deployment, semantics, and team practices required for trusted analytics.
Contact / 05
Bring us the difficult technology problem.
Tell us what you need to build, improve, procure, deploy, or operate. We will respond with a practical next step.