Cloud, DevOps & Data Engineering
Infrastructure-as-code, CI/CD, and data pipelines that make shipping fast and your data fast, correct, and trusted.
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+.
Our process
Assess & Architect
We map your current AWS/GCP/Azure footprint, data sources, and pipeline gaps, then design target Terraform modules and warehouse schemas (Snowflake, BigQuery, or Databricks) before any code ships.
Build Infrastructure
We provision Terraform/OpenTofu modules and Kubernetes clusters, containerize services with Docker and Helm, and wire CI/CD pipelines so every environment deploys the same way, every time.
Pipelines & Warehousing
We build ingestion and transformation pipelines with Airflow, Kafka, Fivetran, or Airbyte, and model the warehouse in dbt on Snowflake, BigQuery, or Databricks with tested, documented dimensional models.
Handoff & Enablement
We load-test autoscaling under real traffic, document golden paths on the internal developer platform, and train your team to run and extend the pipelines and infrastructure themselves.
What we offer
Cloud & IaC
Terraform/OpenTofu on AWS, GCP, or Azure with repeatable, version-controlled infrastructure
CI/CD & Platform Engineering
Automated build, test, and deploy pipelines plus internal developer platforms with golden paths
Kubernetes & Containers
Dockerized services, Helm charts, and resilient, autoscaling clusters
Data Warehouses & Lakehouses
Snowflake, BigQuery, or Databricks builds with clean dimensional models
Data Pipelines & ELT
Reliable ingestion and transformation with dbt, Airflow, Kafka, and Fivetran/Airbyte
Technologies we use
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Cloud, DevOps & Data Engineering
Cloud, DevOps and data engineering is the work of turning infrastructure, deployments, and data pipelines into version-controlled, automated systems instead of manual steps. Prixelo provides it for engineering teams past their first prototype, where hand-built servers, tribal-knowledge deploys, or spreadsheet-fed dashboards have become the bottleneck. Deliverables typically include Terraform or OpenTofu modules for AWS, GCP, or Azure; CI/CD pipelines that test and deploy every change the same way; Docker containers and Kubernetes clusters managed with Helm; and data pipelines built with dbt, Airflow, Kafka, Fivetran, or Airbyte feeding a warehouse on Snowflake, BigQuery, or Databricks. Monitoring and documentation are part of the build, and the engagement ends with a handoff so your team can run and extend the system themselves. Work is scoped and fixed-fee: infrastructure and CI/CD setups start from $8,000, and full data platforms and pipelines start from $25,000+. Final cost depends on how many environments, services, and data sources are in scope.
Questions buyers ask
What does a cloud and DevOps engagement include?
It covers the path from code to production and the data that flows out of it. That usually means infrastructure defined in Terraform or OpenTofu, automated build, test, and deploy pipelines, containerized services on Kubernetes, monitoring and observability, and, where needed, ELT pipelines and a data warehouse. Each engagement starts with an assessment of your current setup, so only the parts you actually need are built.
What is infrastructure as code and why does it matter?
Infrastructure as code means describing servers, networks, and cloud services in versioned files instead of configuring them by hand in a console. Tools like Terraform or OpenTofu then create the same environment every time from those files. The practical benefit is that staging and production stop drifting apart, changes go through code review, and a lost or broken environment can be rebuilt instead of reconstructed from memory.
Does my team need Kubernetes?
Not always. Kubernetes is worth its operational overhead when you run several services that need autoscaling, rolling deploys, and consistent environments. For a single application with steady traffic, a managed container service or platform such as Vercel or Cloudflare can be simpler and cheaper. The recommendation is made during the assessment, based on your stack, traffic, cost, and what your team is comfortable operating.
What is the difference between DevOps and data engineering?
DevOps focuses on how software is built, deployed, and run: infrastructure, CI/CD, containers, and monitoring. Data engineering focuses on how data is collected, transformed, and stored so analysts and applications can rely on it, using tools such as dbt, Airflow, and a warehouse. Prixelo treats them as one discipline, so the platform and the data it produces are built to the same standard.
Will our team be able to maintain the setup after handoff?
Yes, that is the goal of the final phase. Before handoff, autoscaling is load-tested, golden paths on the internal developer platform are documented, and your engineers are trained to run and extend the pipelines and infrastructure. Because everything lives in version-controlled code rather than in one person's head, your team can review, change, and rebuild it with their normal workflow.