About me
Fernando Landero · Senior Consultant & Engineer · Cloud, Data & AI Platforms
I have built and operated software systems for over 15 years — the last 7 focused on cloud and data platforms. Today I work as an independent consultant: teams bring me in to modernize a data architecture, harden their infrastructure, or take an AI system from prototype to production, primarily on Google Cloud and AWS.
I default to portable, open-source building blocks (dbt, Spark, Airflow, Iceberg), so the systems I leave behind are never chained to a single vendor.
Focus areas
| Area | What I bring | Typical stack |
|---|---|---|
| Applied AI & MLOps | AI systems that hold up in production — evaluated retrieval, stateful agents, controlled tool use — and automated ML deployment | LangGraph, Pydantic AI, Gemini / Vertex AI, Databricks, RAG evaluation, Langfuse, OpenTelemetry |
| Cloud & Platform Engineering | Secure architectures defined as code, checked by policy before they apply, with cloud cost under control (FinOps) | Terraform / OpenTofu, Terragrunt, OPA Rego, GitHub Actions, Kubernetes (GKE/EKS), Cloud Run |
| Data Engineering & Pipelines | Batch and streaming pipelines that hold under load, from ingestion to tested transformations | Spark, dbt, Airflow / Cloud Composer, Dagster, Dataflow, Pub/Sub |
| Data & Storage Architecture | Dimensional models, data contracts and storage choices across data warehouses and open-table lakehouses | BigQuery, Iceberg, Delta Lake, Parquet, Kimball, Data Vault 2.0 |
Selected experience
2026–present · Landerox (independent)
Role: Senior Consultant · AI Engineer · Industry: Finance, Energy
Multi-agent reasoning on GCP using LangGraph for financial loan evaluation, prompt caching, and token cost telemetry. Built an automated monitoring agent that diagnoses pipeline failures with local LLMs (Hermes-3) and generates corrective PRs under human review. On AWS, deployed a Medallion lakehouse unifying decentralized sources for multi-agent assistants.
2025 · TCS
Role: Senior Consultant · MLOps & Data Engineer · Industry: Credit Data & Security
Built an internal Python core library with CLI tooling in Vertex AI Workbench to automate ML deployments across Spark, Databricks, and Dataproc. Hardened zero-trust CI/CD pipelines, orchestrated cross-domain PII scanning, and governed BigQuery ingestion under strict compliance.
2024–2025 · Axity
Role: Senior Consultant · MLOps & Data Engineer · Industry: Retail Real Estate
Orchestrated multi-agent Gemini workflows on Cloud Run with Redis session state, replacing OpenDataQnA with a custom orchestrator to eliminate latency bottlenecks. Deployed multi-project GCP landing zones in Terraform, batch/streaming Dataflow pipelines, and automated PII obfuscation routines.
2023–2024 · Acid Labs
Role: Senior Data Engineer · Industry: Retail
Managed a Kubernetes data platform: 200+ custom Python extractors ingesting into BigQuery raw layers, and 1,100+ dbt models building Silver/Gold lakehouse layers for 8 business units. Implemented GKE/BigQuery cost optimization (FinOps), PII isolation in dbt views, and automated Parquet backup routines.
2022–2023 · Sodimac
Role: Senior Data Engineer · Industry: Retail
Built batch and streaming ingestion pipelines (Dataflow, Pub/Sub, Airflow), cross-cloud ELT from Amazon S3 into BigQuery, and containerized weekly ML recommendation model training on Kubernetes while optimizing BigQuery query compute costs.
2019–2021 · NTT DATA
Role: Data Engineer · Industry: Energy, Aviation, Telecom
In the energy sector, implemented distributed ingestion of Salesforce CRM data into a Cloudera/Hadoop data lake using PySpark, HiveQL and Bash to feed Tableau dashboards. For an airline, joined an on-premises to Google Cloud Platform (GCP) migration and built the data layer: Data Vault 2.0 domain modeling under a data mesh approach, Python ETL/ELT pipelines orchestrated with Cloud Composer (Airflow) and Cloud Functions into BigQuery, Scala jobs on Dataproc, and containerized workloads on Kubernetes. Also refactored Scala/Hadoop processing jobs for a telecom operator.
2008–2017 · Banking & Payments
Role: Systems & Data Engineer · Industry: Core Banking
Engineered core banking, billing, and transactional financial systems — establishing strict standards for data consistency, idempotency, and operational reliability.
Get in touch
How I work with teams, and when I am reachable, is on Collaboration.