• Design and build robust data pipelines that integrate data from diverse sources to support risk evaluation and business intelligence • Build streaming data pipelines using Kafka and AWS services to enable real-time data processing and decision-making • Create and operate data services that make curated datasets accessible to internal teams and external partners through APIs and scheduled deliveries • Support data science workflows by building infrastructure that enables model training, feature engineering, and production inference • Implement data quality frameworks including validation, monitoring, and alerting to maintain trust in our data assets • Contribute to CI/CD pipelines and infrastructure-as-code practices to ensure reliable, repeatable deployments • Deliver highly reliable services by following engineering best practices and participating in an on-call rotation to support production systems • Contribute to the data warehouse by creating well-modeled datasets in Snowflake that empower analysts and data scientists • Collaborate with data scientists, analysts, and business stakeholders to understand requirements and deliver solutions that drive value

• Design, build, and maintain efficient ETL/ELT processes and reliable data pipelines in dbt and BigQuery, integrating data from a variety of sources (MongoDB, SQL, Google Sheets, Google Analytics, APIs, etc.); • Build and maintain dashboards and visualizations in Looker Studio and other BI tools; • Ensure data quality, consistency, and accessibility across the organization, implement data quality controls; • Proactively collaborate with different stakeholders across operations, sales, finance and tech to clarify requirements and answer data-related questions.

• Build scalable data pipelines using Python, Spark and Airflow to move data from different applications into our data lake. • Collaborate and influence the long term technology strategy and innovations roadmaps across data engineering areas. • Provide technical leadership, mentorship and growth to junior team members as we scale • Collaborate closely with engineering and product to elevate data generation approaches • Manage the end-to-end life cycle on the development of new features and pipelines • Ideate and contribute to shared data engineering tooling and standards. • Define and promote data engineering best practices across the company.

• Design, implement, and validate ETL/ELT data pipelines–for batch processing, streaming integrations, and data warehousing, while maintaining comprehensive documentation and testing to ensure reliability and accuracy. • Maintain end-to-end Snowflake data warehouse deployments and develop Denodo data virtualization solutions. • Recommend process improvements to increase efficiency and reliability in ELT/ETL development. • Stay current on emerging data technologies and support pilot projects, ensuring the platform scales seamlessly with growing data volumes. • Architect, implement and maintain scalable data pipelines that ingest, transform, and deliver data into real-time data warehouse platforms, ensuring data integrity and pipeline reliability. • Partner with data stakeholders to gather requirements for language-model initiatives and translate into scalable solutions. • Create and maintain comprehensive documentation for all data processes, workflows and model deployment routines. • Should be willing to stay informed and learn emerging methodologies in data engineering, and open source technologies.