DataOps
Pipelines that don't quietly break the dashboard at 3am.
Data teams ship dashboards and models, but the plumbing underneath is often fragile, undocumented, and missing tests. DataOps applies the same engineering discipline to data pipelines that DevOps brought to application code.
We design reliable ingestion, transformation, and orchestration with versioned schemas, automated quality checks, and clear ownership so analytics teams stop debugging the warehouse and start delivering insight.
Capabilities included
Pipeline Orchestration
Airflow, Dagster, or Prefect with retries, SLAs, and dependency-aware scheduling.
Modeling & Transformation
dbt models with tests, documentation, and lineage so downstream consumers can trust the data.
Data Quality
Great Expectations / Monte Carlo / Soda checks at ingestion and transformation boundaries.
Warehouse Operations
Snowflake, BigQuery, or Redshift cost management, query performance tuning, and access control.
Tools we work with
Engagement model
Map & Audit
Inventory pipelines, sources, and consumers; identify the SLAs that actually matter to the business.
Modernize Stack
Migrate to a managed orchestrator and a tested transformation layer - incrementally, never big-bang.
Add Quality Gates
Tests, freshness SLAs, and lineage make breakages visible before consumers see them.
Operate
Ongoing cost, performance, and reliability oversight; new sources added without regressions.
What you get
- Orchestrated ingestion and transformation pipelines
- Modeled data warehouse layer with tests and docs
- Data quality monitoring with alert routing
- Warehouse cost dashboard and optimization plan
Typical results
Ready to talk DataOps?
Book a free call. We will scope the engagement and share a proposal within 24 hours.