dbt analytics engineers and data modeling specialists

Digiventi provides dbt analytics engineers and data modeling specialists who join your team to resolve inconsistent reporting definitions and build reusable warehouse models.

• Dimensional modeling: Define fact-table grain and dimensions around business processes, with explicit handling of historical changes and duplicate records.
• Metric definitions: Translate agreed business rules into shared SQL models or dbt Semantic Layer definitions where appropriate.

Specialists trace discrepancies back to source data and document assumptions so reporting changes have an agreed basis for validation.

Replace duplicated reporting SQL with maintainable dbt projects. Engineers develop staging and intermediate models, then build reporting marts around agreed business requirements. They can work with dbt Core or dbt Cloud on warehouses such as Snowflake and BigQuery.

For existing stored procedures or scheduled SQL scripts, a specialist maps dependencies and separates reusable logic before migration. Reconciliation queries compare outputs at the agreed grain, while incremental models and snapshots address processing volume and historical reporting requirements.

Introduce checks where incorrect data or unreviewed SQL changes put reporting at risk. dbt engineers configure source freshness checks and model tests around business rules, including relationship integrity and accepted values.

• Custom data tests: Check reconciliation totals and business-specific exceptions that standard schema tests cannot capture.
• Pull-request validation: Configure CI jobs using dbt state selection and isolated build schemas to check changed models before merge.

Engineers also define model contracts where downstream consumers need explicit schema expectations.

Keep existing dbt projects maintainable as data volumes and reporting requirements change. A dbt specialist investigates failed runs and incorrect outputs, tracing issues through model dependencies to the affected source or SQL logic.

Performance work uses warehouse query plans and dbt run results to identify expensive models. Engineers review incremental strategies and materializations, then validate changes against expected outputs. Ongoing support can also cover package upgrades and orchestration changes, with regression checks for downstream reports.