Project structure that scales
Without a deliberate staging → intermediate → marts layering, dbt projects sprawl into hundreds of interdependent models that nobody can navigate or refactor safely.
dbt Analytics Engineering ELT SQL
dbt development for trustworthy, tested data models
We build the transformation layer of your modern data stack with dbt — turning raw warehouse tables into governed, tested, documented data products. Our analytics engineers serve US and EU data teams who need reliable marts, clear lineage and fast, cost-aware runs. From greenfield dbt projects to rescuing tangled SQL, we ship maintainable models that analysts and stakeholders actually trust.
We build the transformation layer of your modern data stack with dbt — turning raw warehouse tables into governed, tested, documented data products. Our analytics engineers serve US and EU data teams who need reliable marts, clear lineage and fast, cost-aware runs. From greenfield dbt projects to rescuing tangled SQL, we ship maintainable models that analysts and stakeholders actually trust.
Challenges
Industry challenges we solve
Incremental strategy & cost
Naive full-refresh runs reprocess entire history on every build, burning warehouse credits; choosing the right incremental strategy and managing late-arriving data is genuinely hard.
Testing & data quality
Untested models silently break downstream dashboards; teams struggle to decide what to test, where, and how strictly without slowing every run to a crawl.
Macro & Jinja complexity
Over-clever Jinja and copy-pasted SQL violate DRY and become unreadable; under-using macros leaves logic duplicated across dozens of models.
CI/CD for transformations
Running and testing only what changed (slim CI) against ephemeral or isolated environments is non-trivial, yet without it every pull request risks the whole warehouse.
Lineage, docs & ownership
As models multiply, lineage gets murky, documentation rots and no one owns a given mart — making impact analysis and trust impossible.
Solutions
Solutions we build
Layered dbt architecture
We structure projects into staging, intermediate and mart layers with consistent naming, sources and folder conventions so the project stays legible as it grows.
Cost-aware incremental models
We implement the right incremental materialisations and strategies (merge, insert-overwrite, microbatch), handle late data and full-refresh policy, and cut warehouse spend.
Data-quality framework
We add generic and singular tests, schema contracts, freshness checks and packages so failures surface in CI — not in a stakeholder's dashboard.
Reusable macros & packages
We extract shared logic into well-documented macros and adopt vetted packages (dbt_utils, codegen, dbt_expectations) to keep the codebase DRY and consistent.
CI/CD & environments
We wire slim CI on pull requests, separate dev/CI/prod targets and automate builds so only changed models and their children run and get tested before merge.
Docs, lineage & governance
We generate dbt docs with full lineage, add descriptions, exposures and ownership metadata, and define a governance model your whole team can rely on.
Stack
Technology stack
dbt Core, dbt Cloud, Snowflake/BigQuery/Databricks/Redshift adapters, Jinja macros, tests & snapshots, exposures, dbt docs/lineage, Git, CI.
Compliance
Compliance & regulations
GDPR · tested data quality · lineage/governance · SOC 2
EU
- GDPR — transformations stay inside your warehouse with masking and PII-handling models; we keep data in an EU warehouse region and minimise exposure of personal fields in marts.
- EU AI Act — column-level lineage, dbt docs and tested model provenance give you the transparency and traceability needed for data feeding AI and analytics systems.
- eIDAS — auditable, version-controlled transformation logic and reproducible runs support trustworthy records for regulated identity and signature workflows.
- NIS2 — Git-based change control, CI gates and access-scoped warehouse roles harden your data pipeline as part of essential-entity security obligations.
US
- HIPAA — transforms execute inside your HIPAA-eligible warehouse with no data movement to third-party tools, keeping PHI within the compliant boundary.
- PCI DSS — cardholder data stays in-warehouse; we tokenise or exclude sensitive columns in models and restrict who can build downstream marts.
- SOC 2 — change management through Git, CI tests, environment separation and documented lineage map directly to security and availability controls.
- CCPA/CPRA — PII models, deletion-aware snapshots and clear data lineage make consumer access and erasure requests practical to honour.
Cases
Selected dbt case studies
Crypto · FinTech
EverCoin Bank
Unified crypto-ecosystem hub aggregating multiple tokens — live exchange data, search, charts, direct purchase entry point.
Manufacturing · E-commerce
REHAU
B2B e-commerce and product configurator for a global polymer manufacturer with multi-region pricing, stock and dealer workflows.
Retail · Fashion
SuperStep
Retail POS companion app for a multi-brand boutique chain — ElasticSearch cross-store inventory search, 1C-system integration.
Why YuSMP
Why data teams choose YuSMP for dbt development
Infra-accurate analytics engineering
We know dbt does the T in ELT — it transforms data already in your warehouse, it is not an ingestion tool or an orchestrator — so we design pipelines that fit reality.
Trustworthy, tested data
Every model we ship comes with tests, documentation and lineage, so analysts, executives and downstream systems can depend on the numbers.
Warehouse-native & vendor-fluent
Snowflake, BigQuery, Databricks or Redshift — we tune materialisations, costs and adapter specifics to your platform rather than a one-size-fits-all template.
FAQ
dbt Development FAQ
What is dbt and where does it fit in ELT?
dbt (data build tool) handles the transform step in ELT. After raw data is loaded into your warehouse, dbt runs SQL models to clean, join and aggregate it into analytics-ready tables and views. It does not ingest data and it is not an orchestrator — it transforms data that already lives in the warehouse.
What is the difference between dbt Core and dbt Cloud?
dbt Core is the free, open-source command-line tool you run yourself in your own infrastructure or CI. dbt Cloud is the hosted commercial product that adds a managed scheduler, IDE, CI integrations and a hosted docs/metadata layer. We work with both and help you choose based on team size, governance needs and budget.
What are incremental models and when should we use them?
Incremental models build only the new or changed rows on each run instead of rebuilding the whole table. They dramatically cut warehouse cost and runtime on large, append-heavy datasets. They add complexity around late-arriving data and full refreshes, so we apply them where the volume justifies it and keep smaller models as simple table or view materialisations.
How does dbt handle testing and data quality?
dbt lets you declare tests — generic ones like not-null, unique and relationships, plus custom singular tests — that run as part of every build. Combined with source freshness checks and packages like dbt_expectations, this catches bad data before it reaches dashboards. We design a test suite that is thorough without making runs prohibitively slow.
How do dbt, Airflow and the warehouse fit together?
The warehouse (Snowflake, BigQuery, etc.) stores and computes the data; dbt defines and runs the SQL transformations inside it; and an orchestrator such as Airflow schedules and triggers dbt runs alongside ingestion and other tasks. dbt itself does not schedule pipelines — it is invoked by an orchestrator or by dbt Cloud's scheduler.
Which data warehouses does dbt support?
dbt supports all major cloud warehouses through adapters, including Snowflake, Google BigQuery, Databricks, Amazon Redshift, Postgres and others. We tune materialisations, performance and cost to your specific platform, since each adapter has its own SQL dialect and optimisation levers.
When is dbt overkill?
For a tiny dataset with one or two simple tables and no real transformation logic, a couple of SQL views or a lightweight script can be enough — dbt's project structure, tests and CI add overhead you may not need yet. dbt pays off once you have multiple models, several contributors, recurring quality issues or a need for lineage and documentation.
Ready to build a dbt project your team can trust?
Response within 1 business day. NDA on request.