Data Engineering & Warehousing
Build scalable data platforms with dbt, Snowflake, BigQuery, or Redshift.
The problem
Your data is scattered, pipelines break, and analysts don't trust the numbers. You need a reliable data platform that scales.
Our approach
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.
Outcomes
Reliable, transformation-ready data warehouse
Data quality frameworks with automated testing
Cost-optimized queries and storage
Real-time and batch processing support
Sample deliverables
- 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
Timeline
6–10 weeks for initial platform; ongoing pipeline development.
Tech stack
FAQs
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.
Ready to get started?
Book a thirty minute technical scope call. We will review your requirements and respond with a timeframe and estimate.
Request a scope call