Cloud, DevOps & Data Engineering

Most engineering teams hit the same wall twice: once when infrastructure stops being reproducible, and again when data pipelines stop being trustworthy. We build the layer that fixes both. On the infrastructure side, that means Terraform or OpenTofu modules that turn your AWS, GCP, or Azure environment into version-controlled code, plus CI/CD pipelines that automate the path from commit to production. On the data side, it means dbt models, Airflow- or Kafka-driven pipelines, and warehouses on Snowflake, BigQuery, or Databricks that give analysts and downstream apps numbers they can actually trust.

This work is for engineering teams past their first prototype — the ones where manual server setup, tribal-knowledge deploys, or spreadsheet-fed dashboards are now the bottleneck. A growing startup needs autoscaling Kubernetes clusters and golden-path developer platforms so shipping doesn't slow down as the team grows. A company drowning in exports from multiple disconnected tools needs a lakehouse and ELT pipelines that replace manual CSV wrangling with scheduled, tested transformations.

We treat infrastructure and data as one discipline, not two handoffs — the platform and the data it produces are built to the same standard. Work is scoped and fixed-fee rather than open-ended, so cost tracks to outcome: infrastructure and CI/CD builds start at $8,000, full data platforms and pipelines start at $25,000+.