Data teams replacing scripts and opaque warehouse SQL
Move transformations into versioned models with dependencies, tests, documentation, review, deployment, lineage, and ownership.
dbt Core and dbt platform architecture, SQL models, tests, documentation, lineage, semantic metrics, CI/CD, governance, and operations
dbt analytics engineering services
Rokad designs, builds, migrates, governs, and operates dbt projects across analytical modelling, testing, documentation, lineage, semantic metrics, CI/CD, and data-platform integration.
Platform fit / 01
Designed for teams with a specific platform requirement.
dbt brings software-engineering practices to warehouse and lakehouse transformation. Rokad structures project architecture, sources, staging, intermediate and mart models, materialisations, incremental logic, tests, documentation, exposures, semantic definitions, packages, environments, CI/CD, orchestration, observability, cost, and ownership around trusted analytical products.
Organisations standardising analytical definitions
Create governed business entities, dimensions, facts, metrics, semantic models, naming, contracts, and documentation.
Teams scaling an existing dbt project
Improve project boundaries, performance, incremental models, packages, CI, environments, orchestration, data quality, and developer experience.
Implementation risks / 02
The platform problems Rokad is prepared to solve.
The dbt project mirrors source systems rather than business meaning
Models expose operational tables without durable entities, dimensions, facts, metrics, ownership, or consumer contracts.
Incremental models cannot be rebuilt confidently
Unique keys, late data, schema changes, lookback windows, deletes, backfills, snapshots, and full-refresh behaviour are not designed.
CI validates SQL syntax but not analytical impact
Modified models, downstream dependencies, contracts, tests, row changes, performance, documentation, and BI compatibility lack targeted evidence.
Platform capabilities / 03
What Rokad can implement and operate.
01
dbt Core and managed dbt platform assessment, project architecture, migration, environment, usage, and operating design
02
Sources, staging, intermediate, fact, dimension, mart, domain, data-vault, wide-table, and semantic modelling
03
Views, tables, incremental models, snapshots, seeds, ephemeral models, macros, packages, tests, hooks, and materialisation strategy
04
Source freshness, generic and singular tests, contracts, constraints, unit tests, data quality, reconciliation, and incident integration
05
Documentation, descriptions, lineage, exposures, ownership, tags, groups, versions, deprecation, and catalogue workflows
06
Semantic models, metrics, dimensions, entities, time logic, BI integration, governed definitions, and metric lifecycle
07
Git, code review, CI, slim or state-aware builds, deferral, environments, orchestration, artefacts, observability, performance, cost, and managed operation
Implementation system / 04
The architecture behind a dependable platform delivery.
Analytical model architecture
Sources, staging, intermediate logic, business entities, facts, dimensions, marts, domains, semantic models, metrics, and ownership.
dbt engineering system
Repositories, packages, macros, materialisations, tests, contracts, documentation, state, artefacts, environments, and developer workflows.
Delivery and orchestration
Pull requests, CI, selective builds, schedules, dependencies, freshness, retries, backfills, deployment, promotion, and rollback or roll-forward.
Analytics operations
Model runs, failures, quality, freshness, lineage, performance, warehouse cost, incidents, ownership, support, and roadmap.
Use cases / 05
Where this platform creates practical leverage.
dbt project implementation
Build sources, transformations, marts, semantic definitions, tests, documentation, CI/CD, orchestration, and operating controls.
Legacy SQL and ETL modernisation
Move stored procedures, scripts, views, jobs, and duplicated BI logic into versioned, tested, documented analytical models.
Enterprise metric and semantic programme
Define shared entities, dimensions, measures, time logic, ownership, validation, BI interfaces, and controlled evolution.
dbt performance and cost optimisation
Improve model graph, materialisations, incremental logic, predicates, clustering or partition use, schedules, concurrency, and warehouse selection.
Architecture / 06
Platform-specific engineering decisions and boundaries.
Layering communicates responsibility, not ceremony
Use staging, intermediate, mart, domain, semantic, and other layers only where they clarify contracts, reuse, testing, ownership, and change.
Incremental logic is paired with rebuild and reconciliation paths
Define keys, change detection, late records, deletes, lookback, schema evolution, full refresh, partitions, and historical validation.
CI selects by changed analytical impact
Use state, lineage, contracts, tests, representative data, downstream models, exposures, and performance evidence to validate changes efficiently.
Quality and governance / 07
Production controls are part of the implementation.
One governed metric definition
Business entities, dimensions, measures, time logic, filters, currency, ownership, and semantic contracts are defined once and tested.
Versioned analytical delivery
Models, reports, dashboards, permissions, data sources, environments, tests, deployment, and rollback follow controlled lifecycle practices.
Usable and trustworthy analysis
Freshness, performance, accessibility, row-level security, lineage, documentation, adoption, and decision workflows are measured.
Delivery / 08
A controlled path from assessment to operation.
Assess
Clarify the business outcome, current systems, platform constraints, data, integrations, risks, ownership, and measurable acceptance criteria.
Design
Define the platform architecture, workflow or storefront model, extensions, integrations, security, environments, and migration sequence.
Implement and validate
Build in controlled increments with testing, stakeholder review, observability, documentation, and platform-specific quality controls.
Launch and operate
Deploy safely, transfer ownership, monitor production behaviour, support users, and improve the implementation using operational evidence.
Typical platform deliverables
dbt project, source, model, test, documentation, semantic, orchestration, performance, cost, and risk assessment
Project, modelling, semantic, environment, CI/CD, orchestration, quality, and operating architecture
Production dbt sources, models, snapshots, tests, contracts, macros, packages, and documentation
Semantic models, metrics, exposures, ownership, versions, deprecation, and BI integration
CI, selective builds, orchestration, freshness, observability, backfill, performance, and cost controls
Analytics engineer, BI, data, governance, operator, contribution, and handover documentation
Engagement models / 09
Use the delivery structure that matches the platform work.
Assessment and roadmap
A bounded review of the current platform, requirements, gaps, risks, architecture, and an executable next-stage plan.
Fixed-scope implementation
A defined integration, migration, storefront, application, workflow, or platform outcome with explicit acceptance criteria.
Embedded platform specialists
Specialists working alongside internal product, engineering, operations, marketing, data, or enterprise teams.
Managed platform evolution
Ongoing maintenance, releases, integrations, support, optimisation, governance, and roadmap execution after launch.
Related platforms and services / 10
Compare adjacent platforms or continue into the wider system.
01
Data engineering
Pipelines, platforms, warehouses, analytics engineering, BI, and governed data operations.
02
Cloud and DevOps
Cloud architecture, delivery automation, observability, security, reliability, and platform operation.
03
Managed technology services
Ongoing application, cloud, security, reliability, support, and continuous improvement.
FAQ
dbt analytics engineering services
Platform scope, ownership, licences, data, integrations, security, migration, and long-term operation are clarified before delivery.
01Can Rokad migrate stored procedures and scripts into dbt?
Can Rokad migrate stored procedures and scripts into dbt?
Yes. We inventory logic, dependencies, temporary state, schedules, transactions, parameters, outputs, consumers, performance, and recovery before translating suitable transformations.
02Can Rokad improve an existing dbt project?
Can Rokad improve an existing dbt project?
Yes. We review architecture, duplication, macros, packages, materialisations, incremental logic, tests, contracts, documentation, CI, orchestration, performance, warehouse cost, and ownership.
03Can dbt define shared business metrics?
Can dbt define shared business metrics?
Yes. We can design semantic models and metrics with entities, dimensions, measures, time behaviour, filters, ownership, validation, versioning, documentation, and BI consumption.
04Can Rokad operate dbt after launch?
Can Rokad operate dbt after launch?
Yes. Managed services can cover runs, failures, freshness, tests, incidents, models, documentation, CI, packages, platform changes, performance, cost, and new analytical products.
dbt · Analytics engineering
Build dbt around governed analytical products, not an expanding graph of SQL files.
Rokad can design the project, migrate transformations, create semantic definitions and tests, implement CI/CD, and operate data quality and performance.
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.