• Design and implement scalable ETL data pipelines from hundreds of data sources.
• Monitor and troubleshoot data workflows, ensuring uptime and issue resolution across production pipelines.
• Build and manage data models to enable consistent and scalable access across applications and teams.
• Maintain detailed documentation of data processes, architecture decisions, and pipeline configurations.
• Develop and maintain internal data tools and frameworks to support data modeling, transformation, and validation.
• Optimize storage and retrieval systems for performance and reliability.
• Analyze large datasets to extract actionable insights and support machine learning and analytics initiatives. Ensure data quality, consistency, and security across the platform.
• Participate in code reviews and provide technical mentorship on best practices for data engineering.
• Develop and enforce version control and CI/CD strategies for data workflows and infrastructure code.
• Collaborate closely with our CTO, Data Infra Lead, Product Lead, and team to directly impact product roadmap.

• Master’s degree in Engineering (any), IT/IS, Analytics (any) or related field of study and one (1) year of experience in the job offered or related occupation in which the required experience was gained.
• Must also have demonstrated experience in the following: Data engineering concepts, algorithms, and system design; Programming languages, such as Python, Rust or Scala; SQL; Complex queries, query optimization, and working with various database systems; Big data tools such as Spark; Data pipeline tools such as Dagster, Airflow or Prefect; and Building robust, scalable and performant data pipelines on the cloud (AWS, GCP and Azure).

Compensation