Cloud & Data Engineering Architecture

Design and build scalable, cost-efficient cloud data platforms on AWS, Azure, or GCP that power your analytical and AI workloads. We help organizations move beyond ad-hoc data infrastructure toward well-architected platforms that enable self-service, reduce operational overhead, and scale with demand.

Data infrastructure is the foundation everything else depends on. Poor architecture in the data layer propagates upstream into every analytics report, AI model, and business decision that relies on it. We help organizations get this foundation right.

What We Deliver

• Lakehouse and data warehouse architecture design — Databricks, Snowflake, BigQuery, Redshift, and hybrid architectures
• Data pipeline design: batch, micro-batch, and real-time streaming using Kafka, Kinesis, Pub/Sub, and Flink
• Cloud-native architecture blueprints aligned to AWS Well-Architected, Azure CAF, or Google Cloud best practices
• Multi-cloud and hybrid connectivity patterns, including data mesh and federated governance architecture
• FinOps: cloud cost governance frameworks, rightsizing analysis, reserved capacity planning, and spend attribution models
• Platform migration planning — on-premise to cloud or cloud-to-cloud transitions with zero-downtime strategies
• Data catalog, data lineage, and observability platform design (dbt, Great Expectations, OpenLineage, Monte Carlo)

Platforms and Technologies

We work across the major cloud providers (AWS, Azure, GCP) and the leading data platforms. We don't have a preferred vendor — we recommend the technology that fits your requirements, existing investments, and team capabilities.