Data Analyst
About the team
Data Analytics is part of the Data & Business Intelligence team, which is responsible for enabling data-driven decision-making across the organization end-to-end from planning and analysis to reporting.
We use a Data Lake and computing clusters to analyze large-scale datasets, identify patterns, and monitor key performance metrics.
This role is ideal for someone who is excited about extracting insights and patterns from data, and who can communicate findings clearly and effectively to the right stakeholders to drive meaningful impact.
About the Role
We’re looking for an early-career Data Analyst who’s exceptionally curious, highly creative in problem-solving, and relentless about getting to the truth in data. You’ll work with cross-functional teams to turn business questions into clear analysis, dashboards, and actionable insights.
This role suits someone with a curious mindset who will go the extra mile to solve a hard problem: digging into edge cases, tracing logic across pipelines, validating assumptions with stakeholders, exploring source code and SQL when needed, and even creating simulations to test hypotheses.
What you’ll do
- Analyze business problems using data: interpret questions, define success metrics, and provide insights that drive decisions.
- Write SQL to extract and transform data (joins, CTEs, and performance-aware queries as you grow).
- Build reports and dashboards with the right KPIs, filters, and user context (and improve them based on feedback).
- Support data tagging and collection requirements: help define events/fields needed to answer business questions.
- Use Python for analysis and automation: simple scripts for cleaning, QA checks, and basic analysis workflows.
- Deep-dive to resolve data issues: identify inconsistencies, trace root causes, and collaborate with other teams to fix or mitigate.
- Communicate insights clearly: explain what the data says, what it doesn’t say, and what we should do next.
- Apply experimentation basics: A/B tests, cohort analysis, and user-level trend analysis.
What we’re looking for
Technical competencies
- Comfortable producing reports and dashboards (or strong ability to learn quickly).
- Able to read and write SQL for analysis and reporting.
- Able to write simple Python scripts for data cleaning and basic analysis.
- Basic understanding of KPIs, metrics, and business questions (e.g., funnel, retention, conversion, performance trends).
- Attention to data quality: can spot anomalies and ask the right questions.
Behavioural competencies
- Strong curiosity and willingness to learn (you enjoy exploring "why" until it’s clear).
- Creative problem-solving: can try multiple angles, not just the obvious path.
- Analytical thinking and attention to detail.
- Solid communication skills: can explain findings to both technical and non-technical audiences.
- Can work under supervision while also taking ownership of assigned tasks.
The "extra mile" mindset we value
We value people who don’t stop at the first answer. You demonstrate this by:
- Treating ambiguous problems as an invitation to clarify, frame, and test.
- Willingness to trace issues to root cause, even when it requires effort (e.g., checking upstream logic, reviewing transformation rules, validating with domain owners).
- Proactively using methods like:
- reading documentation and, when needed, reviewing implementation/source logic
- speaking with other teams to understand system behavior and constraints
- creating controlled tests, back-of-the-envelope models, or simulations to validate hypotheses
- building quick tools/scripts to reproduce issues or validate data
- reading documentation and, when needed, reviewing implementation/source logic
Nice-to-haves / advantages
- Active involvement in open-source, technical blogging/writing, hackathons, or building side projects.
- Experience in data QA (checking freshness, completeness, accuracy, and anomalies).
- Familiarity with experimentation concepts (A/B testing, cohorts) even via coursework/projects.
- For fresh graduates: strong academic track record and standout extracurriculars (competitions, leadership, clubs, community projects).
Qualifications
- 0-3 years experience in data analytics, BI, or a strong portfolio of projects (internships count).
- Bachelor’s degree in a quantitative field (e.g., CS, Statistics, Math, Engineering, Economics) or equivalent practical experience/portfolio.
What success looks like in the first 3-6 months
- You reliably deliver SQL queries, reports, and dashboards that stakeholders trust.
- You proactively identify data quality issues and help drive root-cause resolution.
- You can take a loosely defined question, propose an approach, validate assumptions, and communicate insights clearly.
- You build a reputation for being the person who can "figure it out" through structured deep-dives.