• You're the bridge between raw operational chaos and polished analytics. • Building ETL/ELT pipelines that ingest, transform, and serve data at scale • Designing warehouse schemas (star schemas, fact tables, the whole dimensional modeling thing) • Creating pre-aggregated datasets so dashboards load fast and analysts stay happy • Making sure data flows reliably from source systems → transformations → analytics layer • Partnering with analytics team to understand what data they actually need • Optimizing the infrastructure so it doesn't cost a fortune or fall over • Building data quality checks because garbage in = garbage out

• Design, build, and manage our data warehouse, data storage, and data ingestion solutions. • Implement robust and fault-tolerant systems for data ingestion and processing. • Develop canonical datasets to track key product metrics, including user growth, engagement, and revenue. • Understand and translate business needs into data models to support long-term, scalable, and reliable data pipelines • Enhance and maintain the Data Infrastructure using best practices and latest features to ensure high data quality. • Define and manage SLA’s for data sets and processes running in production • Continuously improve our data infrastructure and empower teams with the best data tooling and systems. • Build strong cross-functional partnerships with Data Analysts, Product Managers, and Software Engineers to understand data needs and deliver on those needs. • Participate in data architecture and engineering decisions, leveraging your strong experience and knowledge. • Build lineage and auditability into data pipelines • Be part of a data engineering team that is also responsible for the reliability of the data systems that are built and be available to respond to critical incidents as needed • Ensure the security, integrity, and compliance of data in accordance with industry and company standards.

• Design, build, and operate robust, fault-tolerant, and observable data pipelines using Airflow and modern ELT best practices • Architect, enhance and improve data ingestion, AWS data infrastructure, including Redshift, S3 and monitoring • Partner with Analytics, Product, Finance, Marketing, and Content teams to design canonical, analytics-ready datasets that power key metrics such as growth, engagement, retention, and revenue • Define, implement, and enforce SLAs, data quality checks, observability, and alerting for production data systems. Take shared ownership of data platform reliability, participating in on-call rotations and driving root-cause fixes for incidents • Contribute to design reviews, raising engineering quality, and sharing best practices • Evaluate and introduce data tooling that improves reliability, developer productivity, and analytics velocity • Collaborate deeply with cross functional teams to align event design, source systems, and data contracts

• Lead and support the delivery of data platform modernization projects. • Design and develop robust and scalable data pipelines leveraging AWS native services. • Optimize ETL processes, ensuring efficient data transformation. • Migrate workflows from on-premise to AWS cloud, ensuring data quality and consistency. • Design automations and integrations to resolve data inconsistencies and quality issues • Perform system testing and validation to ensure successful integration and functionality. • Implement security and compliance controls in the cloud environment. • Ensure data quality pre- and post-migration through validation checks and addressing issues regarding completeness, consistency, and accuracy of data sets. • Collaborate with data architects and lead developers to identify and document manual data movement workflows and design automation strategies.