About the Role
This role is essential for scaling the data platform, requiring end-to-end ownership of pipelines, automation initiatives, and ensuring data reliability to support customer workflows and retention.
Responsibilities
- Develop and sustain operational data pipelines utilizing DBT, Snowflake, and contemporary orchestration technologies.
- Manage data engineering features comprehensively from conception to deployment and optimization.
- Enhance and troubleshoot existing pipelines by identifying performance constraints and resolving problems.
- Lead automation efforts within the data infrastructure to speed up processes and minimize manual tasks.
- Offer secondary support to B2B clients by investigating data discrepancies, clarifying complexities, and ensuring data trustworthiness.
- Create and deploy new data ingestion pipelines to broaden data source integration.
- Advance data quality through validation, monitoring, and testing for dependable and precise data outputs.
- Participate in code evaluations, architectural planning, and adherence to data engineering standards.
Requirements
- Minimum of three years in professional data engineering roles.
- Solid expertise in SQL, data modeling, Python, and ETL/ELT methodologies.
- Practical experience with DBT for constructing and managing transformation pipelines.
Nice to Have
- Proficiency with Snowflake.
- Experience using Databricks.
- Familiarity with AWS services such as S3, Lambda, and Glue.
- Knowledge of Prefect or comparable orchestration tools like Airflow or Dagster.
Benefits
- Competitive pay based on expertise, significant ownership and advancement prospects, adaptable scheduling, fully remote team setup, direct engagement with founders and key engineering leaders.
