• Lead the full lifecycle of data engineering projects, from initial requirement gathering with stakeholders to production deployment and monitoring. • Design, develop and maintain complex data transformations, ensuring high data quality and performance using scripting languages like Python, Airflow, DBT and databases such as Snowflake or similar Data Lakes. • Build, scale, and maintain automated workflows using Apache Airflow to manage sophisticated data dependencies. • Maintain high engineering standards through CI/CD implementation and rigorous version control using GitHub. • Implement automated processes for data validation, ensuring high standards of data quality, accuracy, and integrity across all pipelines. • Act as a technical partner to the Analytics, Sales, and Marketing teams, building curated datasets that drive strategic decision-making.

• Design and implement our BigQuery environment • Set up dbt as the home for our transformation logic • Introduce dimensional models for core business entities • Configure native Firestore-to-BigQuery integration • Work with source-system owners, the analyst team, and engineering teams

• Be the bridge between ZAP and Zendesk's customer-facing analytics application team • Drive joint architectural alignment on the data assets that power customer-facing reporting • Set the design patterns for ingestion, modelling, transformation, governance, and consumption • Stay hands-on. Build and review the highest-leverage pipelines and curated datasets yourself • Lead ZAP's transition into an AI-first operating model • Establish engineering best practice for ZAP and influence beyond it • Mentor staff and senior engineers across data engineering and data science

• Collect, manage, and transform large-scale raw data from multiple sources into structured, reliable, and usable formats that meet business and analytical requirements. • Design, develop, and maintain data architectures, pipelines, and applications using appropriate tools and frameworks to support scalable data processing and product development. • Build and maintain robust extract, transform, and load (ETL) processes that ensure data accuracy, efficiency, and availability across systems. • Perform root cause analysis to identify data issues, system inefficiencies, or process gaps, and implement improvements that enhance data quality and operational performance. • Work in an agile Scrum environment by contributing to sprint planning, development tasks, testing, and sprint reviews to deliver reliable and scalable data solutions