• Design and build our data warehouse • Build ETL/ELT pipelines • Take full ownership of the data structure and handling • Take care of our internal dashboard used by several departments at Relai • Implement internal features of the dashboard such as performing actions related to users or transactions • Implement the back-end endpoints related to these actions

• Design, develop, and maintain data pipelines and datastores that support enterprise analytics, data science, and operational workloads. • Lead and support large-scale database migration initiatives, including on-premises to cloud migrations. • Monitor, analyze, and optimize the performance and stability of data layer services and platforms. • Ensure data integrity, quality, and compliance across pipelines and datasets. • Collaborate closely with peers across engineering, analytics, and technology teams. • Guide, coach, and mentor data engineers, BI developers, and analysts. • Design and implement enterprise-scale data solutions with long-term business impact. • Build and maintain data processing solutions using Python and/or Scala. • Work with a variety of data ingestion patterns, including SFTP, APIs, streaming, and batch processing. • Design and support database models optimized for analytical and reporting use cases. • Implement monitoring, alerting, and observability for data pipelines and infrastructure. • Maintain clear and comprehensive documentation of data architectures, pipelines, and processes. • Work within an Agile environment, collaborating through tools such as Jira and Git.

• Maintain and optimize cloud infrastructure to ensure reliability and scalability for data systems. • Extract, transform, and load (ETL) data across various systems to support business operations and decision-making. • Identify and resolve issues within existing systems to enhance performance, stability, and scalability. • Troubleshoot and fix bugs in deployed systems, ensuring minimal downtime and disruption. • Optimize systems and streamline existing processes to improve efficiency and reduce latency. • Build and optimize system deployment structures to ensure smooth, consistent deployments and system performance.

• Work collaboratively with Product Managers, Designers, and Engineers to set up, develop, and maintain critical back-end integrations for data and analytics platform. • Create and maintain new and existing data pipelines, Extract, Transform, and Load (ETL) processes, and ETL features using Azure cloud services. • Build, expand, and optimize data and data pipeline architectures. • Optimize data flow and collection for cross functional teams of database architects, data analysts, and data scientists. • Operate large-scale data processing pipelines and resolve business and technical issues pertaining to the processing and data quality. • Assemble large, complex sets of data that meet non-functional and functional business requirements. • Identify, design, and implement internal process improvements including re-designing data infrastructure for greater scalability, optimizing data delivery, and automating manual processes. • Develop and document standard operating procedures (SOPs) for new and existing data pipelines. • Build analytical tools to utilize the data pipeline, providing actionable insight into key business performance metrics including operational efficiency and customer acquisition. • Write unit and integration tests for all data processing code. • Read data specifications and translate them into code and design documents.