Data Engineering & Warehousing
Build scalable data platforms with dbt, Snowflake, BigQuery, or Redshift.
Diagnosis
What we fix first
Your data is scattered, pipelines break, and analysts don't trust the numbers. You need a reliable data platform that scales.
Platform blueprint
From messy inputs to trusted decisions
Ingest
Map source systems, CDC needs, batch windows, and ownership boundaries.
Model
Build trusted dbt models, naming rules, marts, lineage, and documentation.
Validate
Add quality tests, freshness checks, reconciliation, and alerting.
Serve
Publish analytics-ready datasets for BI, AI, reporting, and operations.
Delivery plan
How the work moves
Audit: We review your current data landscape, identify bottlenecks, and map data flows.
Architecture: Design a modern data warehouse with dbt models, orchestration (Airflow/Dagster), and quality checks.
Implementation: Build ETL/ELT pipelines, data models, and dashboards. Incremental rollout with validation.
Optimization: Cost tuning, performance benchmarks, and SLA monitoring.
Results
Outcomes your team can measure
Reliable, transformation-ready data warehouse
Data quality frameworks with automated testing
Cost-optimized queries and storage
Real-time and batch processing support
Deliverables
What you receive
- dbt models with documentation and tests
- Airflow/Dagster DAGs for orchestration
- Data quality dashboards (Great Expectations or dbt tests)
- Cost monitoring and optimization reports
- Data catalog and lineage documentation
FAQs
Practical questions
Can you work with our existing warehouse?
Yes. We integrate with Snowflake, BigQuery, Redshift, and others.
How do you ensure data quality?
Automated tests in dbt, Great Expectations, and monitoring dashboards.
What about real-time data?
We build Kafka streams and CDC pipelines for near real-time ingestion.