Cloud-native readiness
Readiness means your teams can deploy data services with the same rigor as product services: IaC, CI/CD, policy guardrails, and observable runtimes. We stand up repeatable blueprints on GCP, AWS, and Azure—then migrate Oracle and batch estates into lakehouse targets without losing operational muscle memory.
Representative capabilities—scoped to your cloud, warehouse, and compliance posture.
Methodology, ownership, and runbooks your procurement and platform teams can inspect—across GCP, AWS, Azure, Snowflake, Databricks, Airflow, and legacy sources such as Oracle.
Shared VPC or hub-spoke models, private service connect, and least-privilege project/folder structures. Federation to your IdP so data engineers do not hold standing admin keys.
Baseline modules for logging, backup, and break-glass are documented for auditors from day one.
Terraform or Pulumi for warehouses, buckets, IAM, and Airflow on Kubernetes. Pipelines run plans, policy checks (OPA/Sentinel), and staged applies—so prod changes are peer-reviewed like application code.
Event APIs and schema registries (Avro/Protobuf/JSON Schema) decouple producers from lake writers. Versioned contracts reduce midnight breakage when SaaS vendors tweak payloads.
Not everything moves overnight. We design strangling paths: CDC to cloud object storage, federated queries, and cutover windows—so cloud-native services grow while legacy cores safely shrink.
Return to the data engineering hub for the full platform narrative, or open another enterprise focus area below.
Answers for data leaders, platform owners, and procurement—without hand-wavy claims.
Share your current warehouse, orchestration stack, and success metrics—we'll propose a phased path with clear validation gates.
