Data Analyst
About The Role
As Data Analyst at Circle, you are the senior analytical operator in the Data & Analytics Division — the person who turns the company's data into the answers that decision-makers act on. You will own the dashboards, lead the analyses, and produce the numbers that drive day-to-day decisions across Product, Risk, Marketing, Operations and Finance. You will work hands-on with Pismo transaction data, app telemetry, credit decision data, marketing campaign data and the company's data warehouse — and you will build the AI-powered monitoring agents that watch the portfolio while the team sleeps. This is a hands-on senior role: you will write SQL, model data in the warehouse, ship dashboards, orchestrate agents, and see your numbers shape decisions at the top of the company within weeks of joining.
Key Responsibilities
• Business Intelligence & Dashboards: Build and maintain dashboards in the company's BI tool (Looker, Tableau, Metabase or equivalent) covering portfolio performance, customer behaviour, product engagement, marketing funnel, and operational KPIs. Make the metrics that matter visible to every team.
• Ad-hoc Analysis: Respond to analytical questions from the CRO, CEO and other senior stakeholders. Turn fuzzy questions into clear answers — write the SQL, build the cut, interrogate the result, and present a one-page conclusion that decision-makers can act on.
• Data Modelling: Build and maintain analytical data models in the warehouse (dbt, SQL views, or equivalent). Define metrics consistently, document the logic, and contribute to the single source of truth.
• AI Agents & Active Monitoring: Set up and orchestrate AI agents that actively monitor the business — anomaly detection on portfolio metrics, fraud signal triage, KPI drift alerting, automated daily-numbers commentary, and self-serve Q&A over Circle's data warehouse. Move the team from reactive reporting to proactive, agent-driven monitoring.
• Reporting Cadence: Own the production of recurring management and operational reports — weekly business reviews, monthly portfolio reviews, marketing performance, and regulatory reporting inputs. Make sure the numbers are correct, reconciled and on time.
• Experiment Analysis: Support A/B tests and champion–challenger experiments run by Product, Marketing and Risk teams — help design the measurement plan, build the data pipeline, and analyse the results.
• Data Quality & Validation: Investigate data anomalies, reconcile sources, and partner with Data Engineering to root-cause and fix data quality issues. Trustworthy data is the foundation of trustworthy analysis.
• Stakeholder Partnership: Build strong working relationships with Product, Risk, Marketing, Operations and Finance — understand their priorities, anticipate their analytical needs, and become the first call when they need to understand the numbers.
• Documentation: Document metric definitions, dashboard logic, data sources and analytical methodology. The audit trail and the knowledge base are non-negotiable.
Requirements
Key Requirements
• Bachelor's degree in Statistics, Mathematics, Economics, Finance, Data Science, Computer Science or a related quantitative field. Master's degree is a plus.
• At least 5 years of professional experience in business intelligence, data analytics, or analytical reporting in banking, consumer finance, fintech, e-commerce or another data-rich industry — with demonstrated ownership of analyses that informed senior-level business decisions.
• Advanced SQL — you can write complex joins, window functions, CTEs and performant queries without reaching for a reference, and you can debug other people's SQL too.
• Strong hands-on experience with at least one BI tool (Looker, Tableau, Power BI, Metabase, Superset or equivalent), including LookML / semantic-layer modelling where applicable.
• Working proficiency in Python or R for data analysis, exploratory work and lightweight automation.
• Hands-on familiarity with modern data stack tooling — data warehouse (BigQuery, Snowflake, Databricks or similar), dbt-style data modelling, version control (Git), and CI for analytics.
• Hands-on proficiency with AI productivity tools (Claude, ChatGPT, Gemini and similar). Practical experience setting up and orchestrating AI agents for active monitoring use cases — anomaly detection, KPI alerting, automated commentary, or self-serve data Q&A — using frameworks such as Claude Agents/MCP, n8n, LangChain, LangGraph, Zapier AI, or equivalents. You should be able to describe an agent workflow you have built end-to-end.
• Strong analytical mindset and structured problem-solving — "close enough" is not good enough when business decisions follow your numbers.
• Strong Excel skills for ad-hoc analysis, reconciliations and stakeholder-facing memos.
• Excellent communication — you can take a fuzzy question, drive it to a sharp answer, and explain the answer to a non-technical audience. You can also coach more junior analysts as the team grows.
• Vietnamese (required, native or fluent); English (Professional level — for documentation, cross-functional collaboration and AI-tool fluency).
Benefits
Why Join Us
• Hands-on Work, Visible Impact: Your dashboards and analyses are read by the CEO, the CRO and the board. You see the impact of your work in decisions within weeks, not quarters.
• Real Stack, Real Skills: Cloud data warehouse, modern BI tooling, Pismo transaction data, app telemetry, Visa risk data — graduate from this role with a tool kit and a portfolio that very few analysts in Vietnam have.
• Front Row Seat: Work directly with the Head of Data and senior leadership across Risk, Product, Marketing and Operations. Learn how a credit business actually runs from the inside.
Benefits
• A cool startup working environment
• 5 days working week with flexibility on demand
• 20 days annual leave
• Macbooks to deliver work in style
• Tailored learning and development opportunities to help you fast track your career
• Health & wellness benefits