Cloud Data Warehouse Engineering Services

A data warehouse engineer designs, builds, and maintains the systems that store and organize large volumes of enterprise data for analytics. This includes architecting cloud warehouse platforms like Snowflake or Databricks, engineering ETL/ELT pipelines, optimizing query performance, implementing data governance controls, and ensuring the infrastructure scales reliably to support business intelligence and AI workloads.

Cybic engineers cloud data warehouses on the industry's leading platforms, including Snowflake, Databricks, AWS Redshift, Azure Synapse Analytics, and Google BigQuery. Platform selection is based on your existing infrastructure, workload requirements, compliance needs, and long-term scalability goals, we are platform-agnostic and will recommend the best fit for your organization.

Migration timelines vary based on data volume, source system complexity, and integration requirements. A focused cloud data warehouse migration typically spans 8 to 20 weeks, covering assessment, architecture design, pipeline engineering, data migration, governance setup, and deployment. Cybic structures each engagement with clear milestones so you have full visibility throughout the process.

Governance is embedded at the architectural level and not added after the fact. Cybic implements role-based access controls (RBAC), encrypted data protection in transit and at rest, full audit trails, and compliance frameworks aligned with GDPR, HIPAA, CCPA, and SOC 2 standards. Data ownership policies and governance structures are defined and documented before deployment begins.

ETL (Extract, Transform, Load) transforms data before loading it into the warehouse, while ELT (Extract, Load, Transform) loads raw data first and transforms it within the warehouse using its native compute power. Cybic engineers both ETL and ELT pipelines depending on your platform, data volume, and latency requirements optimizing for performance, cost, and analytical flexibility.

Yes. Cybic builds warehouses designed for downstream integration with BI tools like Tableau, Power BI, Looker Studio, and Domo, as well as AI and ML platforms. We connect your warehouse to CRMs, ERPs, data lakes, and LLM-powered analytics tools via custom APIs and integration layers ensuring your data flows seamlessly across your entire enterprise stack.

Scalability is a core design principle, not an afterthought. Cybic designs infrastructure-agnostic architectures that scale automatically with your data volumes and query demands. Whether running on AWS, Azure, Google Cloud, or a hybrid environment, the warehouse is built to handle growing workloads, concurrent users, and expanding data sources without requiring architectural rework.

Cybic specializes in both. For legacy environments, we modernize existing EDW infrastructure through cloud migration, ETL/ELT pipeline re-engineering, and data lake integration minimizing operational disruption while improving performance, governance, and cost efficiency. For new builds, we architect cloud-native warehouses from the ground up, optimized for AI readiness and long-term enterprise scalability.