Emergentlabsinc1mo ago

Data Engineer

Bangaloremid

EngineeringData EngineeringData Engineer

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Quick Summary

Key Responsibilities

Design, build, and maintain robust, scalable data pipelines and infrastructure Develop ETL/ELT processes to collect, process,

Technical Tools

EngineeringData EngineeringData Engineer

Emergent builds autonomous coding agents that replace traditional software development by generating, testing, and deploying production applications directly from plain-language intent. Our systems run in production at global scale and are used to build millions of real applications.

Since public launch, Emergent has reached $100M ARR in 8 months. 6M+ users across 190+ countries have built 6.5M+ applications on Emergent. We've raised $100M+, backed by Khosla Ventures, SoftBank, Google, Lightspeed, Prosus, Together, and Y Combinator.

We're solving the hard part of AI-driven software creation: correctness, reliability, security, and scale in real production systems. The team is built by repeat founders, Olympiad medalists, IIT & IIM alumni, and leaders from Google, Amazon, and Dropbox.

We're hiring builders who want ownership, speed, and impact at global scale.

Responsibilities

~1 min read

→Design, build, and maintain robust, scalable data pipelines and infrastructure
→Develop ETL/ELT processes to collect, process, and curate structured and unstructured data from various sources
→Collaborate with data scientists, analysts, and product teams to understand data needs and ensure data reliability and accessibility
→Optimize data systems for performance, cost-efficiency, and scalability
→Ensure data quality, security, and compliance best practices are followed
→Implement and maintain data models, data lakes, warehouses (e.g., Redshift, BigQuery, Snowflake), and real-time streaming platforms (e.g., Kafka, Spark)
→Document systems and contribute to internal data engineering standards and playbooks

3+ years of experience in a data engineering or similar backend role
Proficiency in Python, SQL, and one or more data orchestration tools (e.g., Airflow, Dagster, Prefect)
Hands-on experience with cloud data platforms (AWS, GCP, or Azure)
Familiarity with data modeling, warehousing concepts, and distributed systems
Strong understanding of data governance, privacy, and security principles
Experience with tools like dbt, Kafka, Spark, or similar technologies is a plus
Bonus: background in ML pipelines, MLOps, or DevOps practices