Associate Data Engineer

Arkose Labs · Escazú, Costa Rica

01/2025 — Present 

As a Data Engineer in the Customer Success team, I own the data backend that turns raw event data into clean datasets powering an automated BI reporting platform built in Looker Studio. CS teams use it to present value to enterprise customers, and executives use it to monitor portfolio health and renewals. I drive cross-team technical alignment with Professional Services, Account Management, and Solutions Engineering to maintain a single source of truth across reporting use cases, shared definitions, and KPI calculations. I lead the planning and scoping for incoming requests, prioritizing what delivers the most business value and turning it into production-ready datasets, fast.

Key achievements

• Owned and scaled the CS BI platform data backend, the source of truth for product usage and value, adoption and engagement, and customer portfolio intelligence and health. The platform centralizes data from multiple sources (product event data lakes, Zendesk, Gainsight, Pylon, Slack, Jira, Harvest) into a unified PostgreSQL warehouse through dedicated pipelines, serving CS, executives, and stakeholder teams.
• Cut manual reporting time across the team by optimizing queries and schedules; the BI platform now refreshes twice daily (up from once):
  • QBRs: from 2 hours to 10 minutes per customer
  • Monthly Security Reports: from 2 hours to a fully automated Slack-delivered report
  • Post-attack reports: from 2–3 hours to 10 minutes per incident
  • Threat Intel reports for Marketing: from 6–8 hours of manual exports to 30 minutes of centralized data. Recent examples: Threat Actor Patterns Q2 2025 and 2026 Agentic AI Security Report
• Leading my team's AI adoption strategy, primarily on the Claude stack, defining how AI integrates into our current tools and processes. The vision: build intelligence layers that produce hyper-personalized, standardized customer reports that today require heavy manual customization. Early wins are already showing in daily tasks (ticket creation, to-do lists, documentation, coding). I'm excited to lead this transformation and turn frontier technology into real business value. As part of this, I designed and ran an 8-lesson AI upskilling program for a 6-person team covering LLMs, how tokens work, prompting, RAG, evaluation, and agentic workflows, delivered with slide decks and hands-on labs.
• Led end-to-end data engineering for two churn and contraction prevention initiatives, from source system discovery and API integrations to transforming raw → clean → gold datasets in PostgreSQL:
  • Customer Health Score & Quadrant Classification: blends adoption and engagement signals across 20+ metrics into a standardized health score, with custom recommendations sent up to 6 months before renewal so teams can act on risks and opportunities early.
• Planned, scoped, and executed the migration of Python ETL code from ECS Fargate/ECR to Airflow (Amazon MWAA), achieving more reliable runs, better isolation of data processing, stronger QA checks, and less manual work. Designed a phased migration plan with full pipeline inventory and dependencies. In Airflow, DAG pipelines run under one scheduler with clear retries and simple monitoring; heavy jobs were split into smaller tasks to isolate failures. 7 pipelines migrated successfully.
• Currently leading the migration of the Customer Success data backend to Snowflake (~70 tables): scoping the migration plan, mapping dependencies, redesigning data models, and shaping the next chapter of our CS data platform. Aligning with the broader Data team that owns the company-wide migration to ensure consistency in patterns and standards.
• Launched a Security+ certification cohort for my team: scoped phases, led weekly study sessions, gathered materials, and scheduled practice tests to prepare teammates for certification. Sharing knowledge and watching teammates grow their careers is one of the parts of this work I enjoy most.