About the role
TL;DR
Builds scalable data pipelines and models to power analytics, AI, and product features.
- •{"about_the_role": "Data and Analytics Engineering at Coursera builds robust data pipelines and models to power analytics, AI, and product features.
- •This role focuses on creating scalable data pipelines and data models that are crucial for business applications and external data products.", "key_responsibilities": ["Build scalable data models and ELT pipelines using technologies like Airflow, DBT, and Databricks.", "Develop data pipelines and self-serve analytics products, linking them to business outcomes.", "Mentor team members and advocate for stakeholder success, increasing data literacy and resolving data gaps.", "Partner with data scientists and product engineers to define, curate, and govern high-fidelity data.", "Develop new tools and contribute to data platform frameworks, enhancing them with AI-driven capabilities."], "requirements": ["8+ years of experience in data/analytics engineering with expertise in data architecture, pipelines, and reporting.", "Strong experience with relational databases, DRY data modeling, and efficient SQL code generation.", "Proficiency with AWS, Databricks, Delta Lake, Airflow, dbt, Redshift, Datahub (Databricks and dbt preferred).", "Experience with BI tools like Looker or Sigma for self-service reporting solutions.", "Hands-on experience with AI tools such as Claude, Gemini, and Cursor for streamlining data processing."]}
Required skills
SQLPythonAWSDatabricksdbtAirflow
Nice-to-have skills
LookerTableauRedshiftAnthropic API
Domain expertise
edtechai
Tech stack
AirflowdbtDatabricksLookerTableauSQLAWSRedshiftPythonAnthropic API