• Design, implement, and maintain data pipelines (event-based and batch) using modern orchestration tools (e.g., Dagster). • Develop and manage data transformation workflows (rETL) to integrate data with third-party systems such as CRM, CDP, and marketing platforms. • Own and evolve the data architecture, covering ingestion, transformation, storage, and data serving layers. • Design scalable data models and ensure data quality, governance, and performance optimization. • Build data services, APIs, and internal data products to enable real-time and programmatic data access. • Integrate machine learning models and AI capabilities into data pipelines and workflows. • Ensure reliability through monitoring, observability, and continuous improvement of data processes. • Collaborate with product, engineering, and business teams to define and deliver data-driven solutions.

• Design and implement new architecture for big data ingestion, process and storage • Build data pipelines to automate the ETL processes of all the sources we combine • Collaborate with Data Scientists, Software Engineers to define flexible and scalable solutions • Contribute to the infrastructure configuration with our beloved DevOps

• Build, refactor, and maintain pipeline components of data products for a cloud environment • Own the ingestion, storage, cleansing, profiling, transformation, and presentation of data products • Analyze large and complex datasets for quality, accuracy, anomalies, and performance • Own pre-UAT testing for every change using the standard ABC framework • Consultatively interface with analysts and stakeholders on new data requests, enhancements, recommendations, quality, and awareness items • Assess project-specific integration restrictions/options and recommend load strategy, storage targets, data access layer model, and solution architecture • Participate in modeling star-schema data marts, denormalized performant layers, presentation layers, and Data Vault (or other immutable storage) • Organize source code with versioning framework • Create and maintain project-specific reference architecture diagrams and engineering-oriented documentation • Triage emergent data concerns and coordinate with stakeholders through resolution

• Engineer and validate consumer-level attributes from credit bureau and alternative data sources • Apply statistical techniques to uncover insights and improve predictiveness • Monitor attribute performance for stability, compliance, and accuracy • Build dashboards and visualizations (Tableau) to communicate findings • Collaborate with engineering to productionalize prototypes into scalable solutions • Partner with product, risk, and business teams to maximize impact across marketing, risk assessment, and collections • Engage with clients to explain analytics and translate technical results into applicable business insights