• Design, build, and maintain scalable, fault-tolerant data pipelines (batch and/or streaming) for core business and product data. • Ingest data from diverse sources including APIs, databases, event streams, and third-party services, ensuring high data quality and reliability. • Design and manage data models and storage layers (data warehouses, data lakes) that support analytics and downstream use cases. • Partner with analytics, product, and engineering teams to deliver clean, well-documented datasets that enable self-service analytics and experimentation. • Implement monitoring, logging, and alerting to ensure pipeline reliability, performance, and cost efficiency. • Enforce data governance best practices, including access control, privacy, documentation, and data lineage.

• Design and build batch and streaming data pipelines to support automated data ingestion, ML feature engineering and analytics across multiple product domains. • Own end-to-end delivery of complex, ambiguous data initiatives, including architecture, implementation, testing, deployment, monitoring, and documentation. • Develop and evolve the data platform to support large-scale data processing using modern cloud-native technologies. • Automate data operations (validation, quality checks, alerting, backfills, and recovery workflows) to reduce manual effort and improve consistency. • Optimize cost, performance, and reliability of data workloads. • Partner closely with cross-functional teams (Data Science, Product, Engineering) to understand requirements, translate them into technical solutions. • Evaluate and adopt new technologies (new processing engines, storage formats, orchestration tools, GenAI-assisted ingestion) to keep the platform modern and efficient.

• Influence and oversee the enterprise data roadmap, scaling our data ecosystem as our data universe grows. • Define and enforce ETL/ELT standards for data ingestion, transformation, and loading across the organization. • Architect and manage cloud data infrastructure on AWS, including Redshift, DynamoDB, and scalable pipeline patterns. • Establish data cataloging, source system mapping, and data dictionary practices to organize our data universe. • Automate and streamline onboarding of third-party data sources as external service integrations expand. • Partner with analytics teams to support reporting and visualization needs using variety of tools including Tableau. • Collaborate with business partners on requirements gathering and advocate for effective data solutions. • Drive responsible adoption of AI and ML technologies, including LLMs (e.g., Claude, OpenAI), ensuring ethical use and data integrity. • Define and standardize the company’s ML tooling to enable high-value insights at scale. • Lead data governance efforts, including privacy protections, usage policies, and compliance standards. • Optimize query performance across relational and NoSQL database systems. • Evaluate emerging technologies and assess fit for Slate’s business and technical needs.

• Build and maintain efficient, scalable data pipelines to ingest, process and store data from various sources. • Ensure data quality, integrity and availability. • Design and implement database schemas and structures that enable optimal storage, querying and processing of data. • Develop, manage and optimize ETL processes to extract, transform and load data into target systems. • Ensure timely and accurate delivery of data. • Use Snowflake and Azure Data Factory to manage cloud-based data warehouse and data integration processes. • Ensure efficient data storage and accessibility. • Integrate IoT data using Azure IoT Hub to enable real-time data processing and analytics. • Implement and manage data workflows with Apache Airflow to automate and orchestrate ETL processes and other data-related tasks.