Data Analyst

About the role
We're looking for a fresh graduate or early-career Data Analyst on an analytical engineering path. This role blends the best of data analysis and data engineering. You will help turn raw data into trustworthy, well-modeled datasets that:

• make definitions consistent (so "the number" means the same thing everywhere)
• improve data quality and reliability
• enable self-serve analytics and AI-assisted insights across teams (not just dashboards)
  You will also help make the company’s data AI-ready by building well-defined datasets, metrics, and documentation that can be safely used by AI tools (and people) across the company. With the advancement of AI, we value people who have strong fundamentals and clear thinking. Understanding data structures, measurement, tradeoffs, and how to validate results matters more than memorizing tools. You'll learn how to use AI responsibly to move faster, while still owning correctness, data quality, and interpretation. You will collaborate with a diverse ecosystem of engineers, product experts, and business to solve real problems that impact our customers and business outcomes.

Your responsibilities

• Model and transform data for analysis
  • Build and maintain clean, reusable datasets (fact and dimension tables) that power reporting and self-serve analytics.
  • Contribute to a scalable metrics layer: define, document, and align business definitions (for example, "active user", "approval rate", "default rate").
• Support analytics and decision making
  • Answer ad-hoc questions with clear analysis and explainable methodology.
  • Turn common questions into reusable, self-serve assets: dashboards and AI-enabled workflows (a curated dataset + definitions that an AI assistant can query correctly, plus validated example analyses).
  • Create AI-friendly data products (well-defined datasets, metrics, and documentation) that teams can query through AI tools.
• Enable AI-ready analytics
  • Package datasets and metrics so they can be reliably used by AI tools (clear grain, business definitions, data contracts, examples).
  • Write AI-friendly documentation: glossary, metric definitions, common queries, and pitfalls.
  • Partner with AI and platform teams to ensure critical tables are discoverable, permissioned correctly, and safe to use.
• Ensure data quality and reliability
  • Write basic tests, checks, and monitoring for key datasets and critical metrics.
  • Troubleshoot data issues and improve reliability from source to reporting.
• Work effectively with engineering and product
  • Collaborate with data engineers on schema changes, event tracking, and pipeline improvements.
  • Translate ambiguous business questions into measurable analyses, and communicate findings clearly.
• Use AI tools thoughtfully
  • Use AI to accelerate SQL drafting, code scaffolding, and documentation.
  • Validate AI outputs, document assumptions, and protect sensitive data.

Your expertise (must have)

• Fresh graduate or < 1 year of relevant experience (internships, projects, or part-time roles count).
• Solid SQL fundamentals (joins, aggregations, basic window functions).
• One programming language for analysis (preferably Python) with basic skills in:
  • Data manipulation (tables/dataframes)
  • Basic statistics (distributions, sampling intuition, confidence basics)
• Strong analytical thinking:
  • Ability to define a problem, form hypotheses, validate data, and explain results.
• Strong attention to detail and commitment to accurate, reliable outputs.
• Ability to work effectively in a team-oriented environment.

Nice to have

• Exposure to data modeling concepts (star schema, slowly changing dimensions, metrics definition).
• Familiarity with modern analytics stacks (dbt, BigQuery, Snowflake, Looker, PowerBI, Tableau) through coursework or projects.
• Experience creating AI-ready data assets (clean semantic layers, metric definitions, data contracts, documentation, and evaluation or sanity-check checklists) is a plus.
• Experience using AI assistants responsibly to accelerate analysis or analytics engineering work (for example, SQL drafting, code scaffolding, documentation).
• Experience with version control (Git) or basic software engineering practices.
• Understanding of event tracking and product analytics (funnels, cohorts, retention).
• Knowledge of responsible data handling (PII basics, access controls, safe sharing).

What success looks like

• You can independently produce a well-structured analysis with clear assumptions and validation steps.
• You contribute at least one reliable dataset or transformation that becomes a shared building block for analytics.
• Stakeholders can answer more questions with self-serve and AI-assisted exploration, with less back-and-forth.
• A key dataset or metric you built becomes usable through AI tools with consistent answers (validated against a source-of-truth definition).
• You can spot when results look off, debug quickly, and explain the root cause.