• Build CDC pipelines and real-time streaming (Kafka/Flink) • Design and maintain data models (raw to staging to core) • Implement observability, data transformations and quality checks • Own schema design for multi-tenant analytics • Tune query performance (Trino) and optimize storage (Iceberg compaction, indexing) • Support BI and analytics workloads • Contribute to infrastructure, CI/CD • Assist with infrastructure monitoring and observability

• Design, develop, and maintain Azure Data Factory (ADF) pipelines for reliable data ingestion and orchestration • Develop and optimize Databricks notebooks for large-scale data processing and transformations • Implement robust ELT/ETL processes integrating multiple data sources into Snowflake • Perform data modeling using modern approaches, including Data Vault 2.0 • Write, optimize, and maintain complex SQL statements and SQL scripts • Ensure data quality, performance optimization, and scalability across data pipelines • Collaborate with stakeholders, data architects, and downstream analytics teams to deliver high-quality data solutions • Troubleshoot, monitor, and improve existing data workflows in production environment

• Design, develop, and maintain scalable data pipelines on Microsoft Azure • Build and manage real-time and batch data processing solutions using Kafka, Spark, and Python • Develop and optimize ETL/ELT workflows using Azure Data Factory (ADF) • Work extensively with Databricks for large-scale data processing and analytics • Design, develop, and optimize Snowflake data models and queries • Write efficient and optimized SQL for data transformation and analysis • Process and manage structured and semi-structured data, including XML and JSON formats • Collaborate with cross-functional teams to support data-driven solutions • Ensure data quality, reliability, performance, and scalability of data systems • Follow best practices for cloud infrastructure, security, and performance optimization

• Own our Bronze data layer: build and maintain ingestion pipelines from third-party APIs, transform via dbt, orchestrate via Prefect • Partner with Product and Engineering on Silver-layer modeling — ensuring data is clean, documented, and governed as it moves toward consumption • Enable upstream teams to own their Gold-layer data well — establishing best practices, governance standards, pipeline automation, and tooling so teams can build confidently without it becoming the wild west • Troubleshoot pipeline failures and data quality issues, driving toward root cause and long-term fixes • Contribute to platform evolution — identifying opportunities to optimize, refactor, or scale our data infrastructure • Stay informed on developments in the modern data stack and introduce tools and processes that improve our development workflows