Role Description
#### **About the Role**
We’re looking for a Data Engineering / Analytics Intern who wants to build reliable data systems—not just dashboards.
You’ll work on designing and maintaining data pipelines for logs, usage, quality metrics, and customer analytics in a fast-moving environment where schemas and questions evolve constantly. This means ensuring data is accurate, trustworthy, and useful for decision-making across product and business teams.
You’ll collaborate closely with founders, product, and engineering to turn raw data into actionable insights. Your work will directly impact experimentation, reporting, and how the company makes decisions.
If you enjoy working with messy data, building systems end-to-end, and improving data quality, this role will push you to grow quickly.
##### **What You Will Do**
Design and implement ETL/ELT pipelines for logs, usage, and analytics
Build and maintain transformation layers (dbt or similar)
Set up data quality checks and monitoring for key datasets
Model data in warehouses or lakes for analytics use cases
Support experimentation, reporting, and feedback loops with reliable data
Ensure privacy-aware handling of sensitive data (PII)
Document data models, metrics, and pipelines clearly
##### **What We’re Looking For**
Strong SQL fundamentals
Familiarity with ETL/ELT workflows and tools
Exposure to dbt or similar transformation frameworks
Experience with a data warehouse or data lake
Basic understanding of batch vs real-time data processing
Awareness of data privacy and compliance considerations
##### **Founding Mindset**
You think in terms of decisions enabled, not just pipelines built
You ask “who will use this and why?” before modeling data
You take ownership of data reliability and usability
You balance speed of iteration with long-term maintainability
You proactively identify and fix data quality issues
##### **Bonus**
Experience with experimentation or product analytics
Exposure to BI tools or metric layers
Experience working with event-based or high-volume data
##### **What Success Looks Like**
Within 4–6 weeks, you should be able to:
Own a part of the data stack (e.g., usage or quality metrics)
Ship pipelines that are used for real product or business decisions
Catch and prevent at least one major data quality issue
Improve clarity and consistency of key metrics
##### **What You’ll Get**
Hands-on experience building and owning a modern data stack
Direct collaboration with founders, product, and engineering teams
Ownership of meaningful data systems and pipelines
A portfolio of data models, pipelines, and analytics work
A strong pathway into data engineering, analytics, or product data roles
##### **Who This Is Not For**
If you only want to write ad hoc queries
If you avoid ambiguity around metrics and definitions
If you prefer static, slow-changing data environments
If you’re looking for a low-pressure internship
##### **Who Will Thrive Here**
Builders who treat data as a product
Engineers who think in end-to-end data flows
Calm debuggers of broken pipelines and inconsistent metrics
High-agency individuals who take ownership of data quality and outcomes
##### **About the Company**
We’re building the speech intelligence layer for Southeast Asia—turning real-world, accented, code-switched speech into structured, usable outputs for businesses.