• Designs and builds scalable data extracts, integrations, transformations, and data models. • Ensures successful deployment and provisioning of data solutions across required environments. • Designs and implements data architectures and applications that enable speed, quality, and operational efficiency. • Interacts with cross-functional stakeholders to gather and define requirements and translate them into technical designs. • Develops deep familiarity with enterprise datasets, builds domain knowledge, and advances data quality. • Reviews requirements, identifies gaps, and drives resolution with stakeholders. • Identifies and recommends continuous improvement opportunities, ensuring integrations are automated, governed, and observable. • Serves as a key team member in designing and deploying a ground-up cloud data platform and pipeline. • Partners with data scientists to design, build, and maintain reproducible machine-learning pipelines. • Implements CI/CD for data and ML workflows. • Builds and maintains production-grade ML infrastructure such as feature stores, model registries, and data versioning. • Ensures ML models follow best-practice governance, including automated model performance monitoring and alerting. • Designs scalable data pipelines optimized for ML workloads. • Establishes MLOps standards, coding practices, and automation patterns that scale across teams.

• Build infrastructure for ingestion, transformation, and loading an exponentially increasing volume of data from a variety of sources using Spark, SQL, AWS, and Databricks • Building an organic entity resolution framework capable of correctly merging hundreds of billions of individual entities into a number of clean, consumable datasets. • Developing CI/CD pipelines and anomaly detection systems capable of continuously improving the quality of data we're pushing into production. • Dreaming up solutions to largely undefined data engineering and data science problems.

• Design, develop, and maintain data pipeline architectures. • Optimize data ingestion, storage, and processing workflows. • Collaborate with data scientists and analysts to understand data needs and convert requirements into technical specifications. • Ensure data quality and integrity throughout the data lifecycle. • Implement data security and compliance measures. • Monitor and troubleshoot data systems performance issues. • Stay up-to-date with industry trends, technologies, and best practices in data engineering.

• Enable efficient data access by creating and maintaining data pipelines. • Collaborate with ML engineers to design and maintain automation for machine learning training, quality assessment, and model release process. • Build data infrastructure from the vast amount of data for analytics, hypothesis testing and company metrics. • Identify, design and implement improvement to internal processes allowing to optimize data delivery, automate manual processes. • Design new and improve current patterns for building data models and implement necessary modifications.