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Add a dbt developer who can turn warehouse data into modular, tested and documented models—while making business rules reviewable, deployments safer and analytics easier to trust.
Hire a dbt developer to establish or improve the transformation layer between loaded warehouse data and the reports, metrics, data products or machine-learning features that consume it. For broader platform and ingestion work, see our data engineering services; for broader analytics consulting and BI, see our data analytics services.
Set up repositories, environments, adapters, targets, naming standards, folder structure, packages, permissions and deployment workflows for the approved dbt edition.
Build staging, intermediate and mart models with explicit grain, reusable logic, dimensional patterns, Jinja macros and ownership aligned to business domains.
Add source freshness, generic and custom tests, model contracts, descriptions, exposures and lineage so failures and downstream impact are visible.
Implement pull-request checks, slim/state-aware builds, isolated schemas, scheduled jobs, alerts and controlled promotion through development, staging and production.
Inventory stored procedures and transformation jobs, preserve rule traceability, rebuild suitable logic in dbt and reconcile output before a staged cutover, partnering with our hire ETL developers team when source-side work is the larger constraint.
Tune materializations and incremental models, manage backfills and schema change, reduce unnecessary warehouse scans, investigate failures and maintain runbooks.
Our dbt developers bring deep technical expertise across modeling, testing, CI/CD and warehouse-native transformation engineering.
Development across both runtime models, matched to the environment and release track you actually operate.
Warehouse-native SQL and templated logic that stays DRY and consistent across the project.
Staging, intermediate and mart layers built around explicit, agreed grain.
Executable expectations and visible lineage so failures and downstream impact surface early.
Pull-request checks, state-aware builds and controlled promotion through every environment.
Materialization tuning, safe backfills and warehouse-scan reduction for models that run at scale.
Review a representative role profile, then request two or three current CVs matched to your dbt edition, warehouse, adapter, orchestration, Git workflow, modeling conventions, business domains, security constraints and working-hour overlap.
DreamzTech will replace a blueprint with a verified client case only when the dbt contribution, technology, result and permission are documented. Until then, every card below is a solution blueprint, not a completed client engagement.
Environment: Snowflake or BigQuery
Core Technology: dbt, Git, CI, BI semantic layer
Solution blueprint, not a client case: conflicting finance logic is traced to source and grain, rebuilt as governed staging and mart models, protected with tests and contracts, and reconciled against approved statements. Accepted on signed metric definitions, documented exceptions and repeatable reconciliation—not an invented accuracy percentage.
Environment: Redshift, Synapse/Fabric or Databricks
Core Technology: dbt, orchestrator, warehouse-native SQL
Solution blueprint, not a client case: stored procedures and scheduled SQL are inventoried by dependency and owner, migrated in waves, compared at row and aggregate levels, and cut over with rollback steps. Accepted on agreed parity thresholds, run-time checks and operational handoff.
Environment: Modern cloud warehouse
Core Technology: dbt, source freshness, snapshots, semantic metrics
Solution blueprint, not a client case: product events, subscriptions and billing data are modeled at declared grains with late-arriving-data rules, snapshots, tests and governed measures. Accepted on stakeholder sign-off, freshness visibility, CI checks and stable downstream contracts.
Simple & Transparent Pricing | Fully Signed NDA | Code Security | Easy Exit Policy
A useful matching call begins with what data is already loaded, which business logic must become governed and who signs off that a model is correct. Share the current project, DAG, run history, incidents and highest-risk downstream assets.
Share your warehouse and dbt project and we will design the fastest path to modular, tested, production-ready models.
Our dbt developers bring deep technical expertise across modeling, testing, CI/CD and warehouse-native transformation engineering. Need warehouse-specific engineering too? See our hire Snowflake developers and hire Databricks developers pages.
| dbt Products & Runtimes | dbt Coredbt Cloud / dbt Platformdbt CLIApproved Release Tracks |
| Warehouses & Lakehouses | SnowflakeDatabricksGoogle BigQueryAmazon RedshiftAzure SynapseMicrosoft FabricPostgreSQL |
| SQL Modeling & Templating | Warehouse-Native SQLJinjaYAMLMacrosPackagesStar SchemaData Vault |
| Quality & Data Contracts | Source FreshnessGeneric/Custom/Unit TestsModel ContractsSodaGreat ExpectationsElementary |
| Documentation & Lineage | DescriptionsDocs GenerationExposuresOwnersTagsLineageGovernance Tools |
| Version Control & CI/CD | GitGitHubGitLabAzure DevOpsPull RequestsIsolated CI SchemasState-Aware Selection |
| Orchestration & Scheduling | dbt Jobs/SchedulerApache AirflowDagsterPrefectAzure Data Factory |
| Ingestion & ELT Integration | FivetranAirbyteStitchMatillionAWS GlueAzure Data Factory |
| Semantic Layer & BI Consumers | dbt Semantic LayerPower BITableauLookerQlikQuickSightSuperset |
| Automation & Observability | PythonShell ScriptingAPIsRun ResultsMonte CarloDatadog |
| Cloud, Security & Collaboration | AWSAzureGoogle CloudIAM/SSOSecretsJiraConfluenceSlack |
Hire dedicated Databricks developers for your project with our quick, efficient, and hassle-free hiring process. Build your data-driven team faster and accelerate innovation by onboarding top Databricks professionals.
Tell us your warehouse, dbt edition and model backlog. We will quickly match the right dbt talent to your project.
We connect you with pre-vetted dbt developers ready to deliver. Review profiles, interview, and select the best fit for your transformation layer.
Confirm a realistic start date once availability, interviews, contracting, repository and warehouse access, environment setup and business-owner availability are known.
Hire dbt developer(s) who deliver governed, tested transformation layers across various industries to help businesses trust their numbers.
dbt turns transformation logic into software that can be reviewed, tested and operated. DreamzTech can connect the dbt developer to data engineers, architects, analysts, BI developers, QA and domain owners when the backlog crosses role boundaries.
Share your warehouse, dbt edition, current DAG, highest-risk models and delivery gap. We will respond with the likely developer profile, readiness questions and a practical first scope.
Got questions about hiring a dbt developer? Explore the FAQs below.
A dbt developer builds and maintains SQL-based transformation models inside a data warehouse or lakehouse. Typical responsibilities include project structure, model grain, reusable macros, tests, documentation, lineage, CI/CD, job operations, incremental processing, performance tuning and collaboration with the people who own business rules.
A dbt developer specializes in the transformation layer and dbt workflow. An analytics engineer often combines that work with metric design and close collaboration with analysts. A data engineer usually owns broader ingestion, infrastructure, orchestration and platform reliability. Titles overlap, so DreamzTech matches the profile to the actual backlog.
Choose based on the environment you operate. dbt Core can fit teams that manage their own runtime and orchestration. The dbt platform adds managed development, scheduling, CI and governance capabilities that vary by plan and release track. The developer should understand your edition, adapter, deployment model and security constraints.
Yes, when the transformation logic is suitable for execution in the analytical platform. The work should inventory dependencies, preserve rule ownership, compare old and new outputs, plan backfills and cut over in controlled waves. Source extraction, streaming or infrastructure changes may also require an ETL developer or data engineer.
They make expectations executable through source freshness checks, tests, contracts, documentation and lineage, then run affected models in isolated CI environments before changes merge. Production safety also requires access controls, alerts, reconciliation, rollback or recovery steps and named owners for business-rule exceptions.
Cost depends on seniority, dbt edition, warehouse, model count, migration complexity, orchestration, working-hour overlap and support responsibility. DreamzTech publishes a starting rate of $20 per hour or $3,200 for a 160-hour monthly allocation for this role; fixed projects require discovery.
Profile matching can begin after the dbt edition, warehouse, project condition, responsibilities and engagement model are clear. The actual start date depends on availability, interviews, contracting, repository and warehouse access, environment setup and stakeholder availability, so DreamzTech confirms a realistic date rather than promising an automatic start time.