Role Description Join Fullthrottle.ai, a cutting-edge Marketing Technology SaaS company, where our global software team operates seamlessly across 5 time zones and 6 countries, working collaboratively in a location-independent environment. We are currently seeking a dynamic and self-directed, mid-level Data Engineer (ETL/SQL) with a minimum of 3 years of ETL development experience to contribute to our innovative projects. The ideal candidate should possess strong communication skills, a solution-oriented mindset, and the ability to excel in a fast-paced Continuous Development Agile Environment. Responsibilities - ETL Responsibilities (80% of role) - Manage ETL process, translate complex business requirements into scalable and efficient data solutions. - Investigate and troubleshoot data and user-related system errors to ensure data integrity. - Implement ETL processes to extract, transform, and load data from multiple sources into a data warehouse. - AI Data Preparation / Insights (20% of role) - AI Data Wrangling and Preparation - Cleaning, transform, and prepare large and complex datasets. - Handling missing values, outliers, inconsistencies, and data quality issues. - Data scaling, normalization, encoding, and feature engineering techniques. - AI Data Planning to achieve FT’s AI Strategy - Applying ML models to our prepared Data to gain insight. - Augmenting Models with our data to enable achievement of AI strategies. - Designing, training, evaluating, and deploying ML models, integrating AI solutions into applications, and working with large datasets. Qualifications - Strong proficiency in Python, familiarity with libraries like Pandas, NumPy, Scikit-learn, TensorFlow, Keras, PyTorch. - Excellent skills in writing and optimizing complex SQL queries for data extraction, manipulation, and analysis from relational databases. - Experience with R is a plus, though not a requirement. - Hands-on experience with various AWS Database & ETL services, particularly: S3, Python code, Lambda, Cloud Formation, and other AWS serverless resources. - Active participation in Agile teams, collaborating with team members to review user stories, estimate effort, and contribute to sprint reviews. - Solid understanding of various ML algorithms (supervised, unsupervised, reinforcement learning). - Experience in training, evaluating, and deploying ML models is a plus. - Knowledge of model selection, hyperparameter tuning, and cross-validation techniques. - Familiarity with ML frameworks and libraries mentioned above. - 2+ years of experience in a similar ETL role is essential. Exposure to AI ML models and AI data solutions delivery is a major plus. - Remote Team Experience: 1+ years of experience working in a distributed remote technology team is a plus. Personal Profile - Self-directed and self-motivated with a proven ability to deliver results. - Strong communicators, capable of summarizing key deliverables and meeting daily deadlines. - Experienced in a fast-paced Agile/Scrum development environment. - Comfortable working in a distributed development environment with remote colleagues. - Proven team players. Remote Minimum Requirements - Reliable high-speed internet connection. - Fluent written/spoken English. - Located within +/- 6 hours of US Eastern Standard Time.

• Diseñar y mantener modelos de datos analíticos en DBT, asegurando la trazabilidad, eficiencia y calidad de los datasets. • Construir y optimizar consultas SQL avanzadas en BigQuery, incluyendo la creación de tablas, vistas, rutinas y queries programadas (scheduled queries). • Definir, estandarizar y documentar métricas y KPIs para garantizar su consistencia en toda la organización. • Colaborar con los equipos de negocio para entender necesidades analíticas y traducirlas en modelos de datos escalables y reutilizables. • Desarrollar y mantener dashboards y visualizaciones en Metabase y Tableau, asegurando su alineación con los modelos analíticos. • Implementar procesos de testing y validación de datos (data quality) dentro de DBT. • Contribuir a la mejora continua de la arquitectura de datos junto al Senior Data Engineer. • Participar en la definición de buenas prácticas de versionado, testing y documentación en Git.

• Own the Transformation Layer: Design, build, and maintain complex dbt models to power internal BI and external customer analytics • Pipeline Management: Manage and monitor data ingestion pipelines to ensure high availability and low latency • Performance Tuning: Optimise cloud data warehouse costs and query performance (clustering, partitioning) for sub-second response times • Data Quality & Testing: Build and maintain automated testing frameworks (dbt test, Great Expectations) to proactively catch data issues • DataOps: Maintain CI/CD pipelines (GitHub Actions) for data deployment, applying software engineering principles to data workflows • Collaboration: Partner with Data Analysts to provide clean models and work with the Data Engineering Lead on architectural infrastructure decisions • Technical Documentation: Document data models, macros, and transformation logic clearly to ensure team scalability

• Build, maintain, refactor and optimize data models in our dbt project • Serve as the architect for our dbt project as a whole • Create a consistent user experience across our warehouse and BI tools • Mentor and uplevel analysts and data engineers in data modeling and dbt best practices • Partner with the Data Engineering team to manage our data stack • Be a champion of data privacy and quality