Lead the evolution of our data stack, from pipelines to platform, building the infrastructure that powers everything from product intelligence to financial reporting and self-serve analytics
Job Summary
Lead the evolution of our data stack, from pipelines to platform, building the infrastructure that powers everything from product intelligence to financial reporting and self-serve analytics.
Collaborate with Product, Analytics, and Engineering to build scalable systems that unlock data value from multiple sources like backend databases, event streams, and marketing platforms.
Define data quality and security frameworks to measure and monitor quality across the organization, promoting data engineering best practices.
Matching Summary
Lead the evolution of our data stack, from pipelines to platform, building the infrastructure that powers everything from product intelligence to financial reporting and self-serve analytics.
Skills & Requirements
Must-have
SQL and data modeling
Snowflake data warehouses
DBT for transformations
Airflow, Prefect, Airbyte, Fivetran, Kafka
AWS cloud experience
Python and Unix/Linux scripting
API experience
Nice-to-have
Terraform, Docker, containerized workflows
Agile methodologies (SCRUM, Kanban)
Real-time ETL (Kafka streaming, AWS Kinesis)
Key Requirements
Bachelor's or Master's degree
7+ years in Data Engineering or Analytics Engineering
Strong problem-solving skills
Experience with AWS (S3, RDS, Redshift, etc.)
Experience building and supporting semantic layers for self-serve analytics
Proficiency in BI tools like Looker, Tableau, or Sisense