Data Platform Engineer - Hybrid, Self-Serve Analytics

Otter.ai; Mountain View (CA); On-site; USD 155,000 - 230,000; Full time

Job summary

Otter.ai is seeking a Data Engineer to join the Data Platform team and build core data foundations powering analytics, experimentation, and decision-making across the company.

You will design and own data models, pipelines, and platforms that enable self-serve analytics and trustworthy insights at scale, partnering with Growth, Product, Sales, Marketing, Data Insights, and Engineering.

Qualifications

• 2+ years of experience in data engineering.
• Bachelor's degree in Computer Science or equivalent.
• Hands-on coding mindset and ability to drive projects to completion.
• Strong Python and SQL skills.
• Experience with cloud data warehouses and ETL/ELT pipelines.
• Familiarity with workflow orchestration tools like Airflow/dbt.
• Solid data modeling skills for analytical workloads.

Responsibilities

• Build and own foundational data models enabling self-serve analytics and key business metrics.
• Design, operate, and scale reliable data pipelines and platforms.
• Partner with stakeholders to translate business needs into trusted data products.
• Influence data collection and logging standards across production systems.
• Establish best practices for data modeling, quality, and governance.
• Implement data quality checks, statistical validation, and anomaly detection.
• Provide technical leadership to improve data reliability and literacy.

Skills

Python

SQL

Data modeling

ETL/ELT pipelines

Hands-on development

Education

Bachelor's degree in Computer Science or equivalent

Tools

Snowflake

BigQuery

Databricks

Airflow

dbt

AWS

Job description

Otter.ai is seeking a Data Engineer to join the Data Platform team and build core data foundations powering analytics, experimentation, and decision-making across the company.

You will design and own data models, pipelines, and platforms that enable self-serve analytics and trustworthy insights at scale, partnering with Growth, Product, Sales, Marketing, Data Insights, and Engineering.