What you’ll be doing
Responsible for designing, developing, and implementing our backend and machine learning data infrastructure -- including event-based data ingestions, stream processing, data warehouse, data pipelines, visualization, analytics and applications
Build analytics tools that utilize the data pipeline to provide actionable insights into customer acquisition, operational efficiency and other key business performance metrics and develop and maintain distributed data pipelines for ETL, feature engineering, and model running
Create data tools for analytics and data scientist team members that assist them in building and optimizing our product into an innovative industry leader, and keep our data separated and secure across national boundaries through multiple data centers and AWS regions
Work with data and analytics experts to strive for greater functionality in our data systems and our end-to-end data processing, troubleshooting, and problem diagnosis, performance benchmark, load balance, and code reviews
- Mentor other data engineers, providing technical guidance, sharing best practices, and supporting their professional development
- We’ll be excited if you have
- 5+ years of professional experience in data engineering, software engineering, or a closely related field, with significant experience designing and maintaining production data or machine learning systems
Strong proficiency in Python and SQL, including advanced querying, data transformations, and performance optimization
Experience building and maintaining large-scale ETL/ELT pipelines and distributed data processing systems
Hands-on experience with modern data warehousing technologies such as Snowflake, BigQuery, or open table format such as Iceberg
Experience with data processing frameworks such as Apache Spark, Flink, or equivalent technologies
Familiarity with workflow orchestration tools such as Apache Airflow, Dagster, or Prefect
Experience with cloud infrastructure, preferably AWS or GCP
Strong understanding of data modeling, database design, and data architecture principles
Experience implementing data quality, monitoring, and reliability practices in production environments
Strong analytical and problem-solving skills with the ability to independently drive complex technical projects
Excellent communication and collaboration skills, with the ability to work effectively across technical and non-technical teams
Demonstrated leadership potential and a genuine interest in growing into a people management role, including mentoring engineers, building teams, and driving technical strategy
