Senior Data Engineer

As a Senior Data Engineer at Manifest Global, you will play a crucial role in owning significant parts of the data platform end to end - including ingestion, transformation, warehouse, and activation. Your responsibilities will involve working closely with Principal Engineers, Product, and business stakeholders across all four brands to ensure the data infrastructure is a genuine competitive advantage. Here are the key responsibilities you will undertake: - AI Infrastructure for Data Engineering: - Design and build AI-assisted development tooling to accelerate data assets' time-to-production. - Build intelligent data quality and anomaly detection systems for AI-driven monitoring. - Implement AI-augmented data cataloguing and lineage for automated documentation generation. - Develop AI-powered pipeline debugging and root cause analysis tools. - Maintain the infrastructure supporting AI features across brands. - Data Warehouse Design, Cost & Maintenance: - Own significant portions of the Snowflake data warehouse, including schema design and performance optimization. - Design cross-brand data primitives for consistent data layers across Cialfo, BridgeU, and Kaaiser. - Optimize Snowflake costs and ensure the warehouse handles increasing data volumes without performance degradation. - ETL/ELT Pipelines, Scheduling & Transformation Logic: - Design, build, and maintain production-grade data pipelines for ingestion and transformation. - Manage Snowflake Task DAGs for dependency-chained task graphs. - Own Airtable as an operational data layer for sync workflows. - Build and own reverse ETL workflows for operational tools. - Data Quality & Reliability: - Define and enforce data quality standards across datasets. - Build monitoring and alerting systems for problem detection. - Document data lineage and technical decisions for maintainability. - Enhance the existing BI layer for non-technical stakeholders. In terms of qualifications and experience, you are required to have: - Bachelor's degree in Computer Science, Engineering, or related field. - 5+ years of experience building and maintaining production-grade data pipelines and warehouses. - Strong expertise in data warehouse design, advanced SQL, and modern data stack tools like Snowflake and dbt. - Hands-on experience with Snowflake-native features and BI tools like Metabase. - Proficiency in working with AI/LLM tooling and familiarity with MLOps or AI infrastructure patterns. As a candidate, you should possess the following skills and qualities: - Ability to diagnose pipeline failures and prevent them from reoccurring. - Strong communication skills with non-technical stakeholders. - Proficient in documenting work for maintainability. - Comfortable working in a multi-brand environment. - Enthusiasm for leveraging AI as a force multiplier in engineering workflows. Manifest Global is at the forefront of building infrastructure for global human capital mobility, and as a Senior Data Engineer, you will play a pivotal role in shaping the data platform to support the company's growth and success. As a Senior Data Engineer at Manifest Global, you will play a crucial role in owning significant parts of the data platform end to end - including ingestion, transformation, warehouse, and activation. Your responsibilities will involve working closely with Principal Engineers, Product, and business stakeholders across all four brands to ensure the data infrastructure is a genuine competitive advantage. Here are the key responsibilities you will undertake: - AI Infrastructure for Data Engineering: - Design and build AI-assisted development tooling to accelerate data assets' time-to-production. - Build intelligent data quality and anomaly detection systems for AI-driven monitoring. - Implement AI-augmented data cataloguing and lineage for automated documentation generation. - Develop AI-powered pipeline debugging and root cause analysis tools. - Maintain the infrastructure supporting AI features across brands. - Data Warehouse Design, Cost & Maintenance: - Own significant portions of the Snowflake data warehouse, including schema design and performance optimization. - Design cross-brand data primitives for consistent data layers across Cialfo, BridgeU, and Kaaiser. - Optimize Snowflake costs and ensure the warehouse handles increasing data volumes without performance degradation. - ETL/ELT Pipelines, Scheduling & Transformation Logic: - Design, build, and maintain production-grade data pipelines for ingestion and transformation. - Manage Snowflake Task DAGs for dependency-chained task graphs. - Own Airtable as an operational data layer for sync workflows. - Build and own reverse ETL workflows for operational tools. - Data Quality & Reliability: - Define and enforce data quality standards across datasets. - Build monitorin