Collaboration
I work directly with engineering teams to design architectures, write production code, and automate platforms across data, cloud, and AI systems.
I integrate into your existing workflows (pull requests, Slack, architecture reviews) as an embedded senior engineer. My technical focus covers three areas:
- Data & Applied AI Engineering: Building resilient batch and streaming pipelines (Spark, dbt, Dagster) and deploying production AI workloads (stateful agent graphs, RAG retrieval architectures, and OpenTelemetry / Langfuse telemetry).
- Cloud & Platform Engineering: Implementing declarative Infrastructure as Code (Terraform / Terragrunt), zero-trust CI/CD pipelines (GitHub Actions), and policy-as-code guardrails (OPA Rego) on Kubernetes and serverless runtimes.
- Technical Advisory & FinOps: Evaluating architectural trade-offs (open-source versus managed cloud services), reducing technical debt, and instrumenting cloud cost and latency profiles.
Recent client work focuses on Banking, Retail, and Logistics — operating under strict regulatory, security, and data isolation constraints.
Frequently Asked Questions
What is your typical engagement model?
I work as an independent contractor. I integrate with engineering teams on a project basis, ranging from short-term architecture audits (2-4 weeks) to acting as an embedded technical lead or senior engineer for several months (3-6+ months).
What time zones do you support?
I operate within a broad window, generally from 05:00 AM to 10:00 PM (Caracas time / UTC-4). This allows me to overlap with European mornings, the entire North American business day (EST and PST), and all LATAM time zones. The hours at either end are there for overlap rather than for meetings; the availability pill on the home page shows the typical shape of a day inside that window.
Do you work independently or bring a team?
I work solo: no agency, no subcontracting. I integrate directly with your engineering team to collaborate on code and architecture.
Do you only work with Google Cloud Platform (GCP)?
While my deepest experience is on Google Cloud (GCP), I regularly design and deploy architectures across both GCP and AWS. By relying on open-source data engines (dbt, Spark, Dagster, Iceberg) and portable frameworks (Pydantic AI, LangGraph, FastAPI), systems stay portable and avoid vendor lock-in.