Build data pipelines that scale.
Modern data engineering with dbt, automated testing, and real-time monitoring. Transform raw data into trusted insights--reliably, every time.
Get StartedBuild data flows that scale.
Data pipelines that work.
We build modern data pipelines that transform messy, siloed data into trusted business assets. No more broken ETL jobs, undocumented transformations, or data quality nightmares.
Our approach combines best-in-class tools like dbt, Airbyte, and Great Expectations with rigorous engineering practices. Version control, automated testing, and comprehensive documentation are standard--not extras.
The result? Data teams that ship faster, analysts who trust the numbers, and stakeholders who get insights when they need them. Your data infrastructure becomes a competitive advantage.
The Omnicliq advantage.
Modern Stack Expertise
Deep experience with dbt, Airbyte, Fivetran, Airflow, and cloud warehouses. We know what works at scale.
Data Quality First
Automated testing at every layer. Schema validation, freshness checks, and business logic tests catch issues before they propagate.
Self-Documenting Pipelines
Auto-generated data catalogs, lineage graphs, and column-level documentation. No more tribal knowledge.
Version Control Everything
All transformations in git with PR reviews, CI/CD, and automated deployments. Rollback any change instantly.
Production-Ready Monitoring
Real-time alerting, SLA tracking, and proactive notifications. Know about problems before your stakeholders do.
Knowledge Transfer Included
We train your team to own and extend the pipelines. No vendor lock-in, no black boxes.
Our proven process.
Discovery & Audit
Map all data sources, document business logic, and identify gaps in current architecture. Define success metrics and SLAs.
Architecture Design
Design the ELT pipeline, transformation layers, and testing strategy. Choose optimal tools for your use case and scale.
Build & Test
Implement pipelines with comprehensive testing. Staging environment for validation before production deployment.
Deploy & Monitor
Production rollout with monitoring, alerting, and runbooks. Train your team and establish support protocols.
Common questions.
Data engineering involves designing, building, and maintaining the infrastructure and pipelines that move and transform data from source systems to analytics platforms. It's the foundation that makes analytics and ML possible.
dbt (data build tool) is a transformation framework that enables analytics engineers to transform data using SQL. It provides version control, testing, and documentation for your data models--making transformations maintainable and reliable.
ELT (Extract-Load-Transform) is preferred for modern cloud warehouses as it leverages their processing power. Load raw data first, then transform using SQL. ETL is still relevant for complex transformations or legacy systems.
We implement automated testing with dbt tests, Great Expectations, and custom validation rules. Schema validation, freshness checks, and business logic tests run on every pipeline execution. Monitoring alerts catch issues before they impact downstream reports.
Typical implementations take 4-6 weeks depending on data source complexity, transformation requirements, and testing needs. We prioritize getting a production-ready MVP live quickly, then iterate.
Ready to modernize your data pipelines?
Let's discuss how dbt and modern data engineering can transform your data operations. Get reliable, tested, documented pipelines that scale.
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