• Work closely with Full Stack developers, other data engineers, and data scientists to deliver production-ready data via APIs or ML models. • Implement the architecture of a data platform (Data Lakehouse) and maintain the data lifecycle, ensuring integrity at every stage of the pipeline. • Configure and optimize Apache Iceberg tables to support ACID transactions, schema evolution, and hidden partitioning, ensuring consistency and concurrent reads. • Build automated Airflow workflows with preprocessing and metadata extraction using a factory pattern. • Implement data contracts and automated pipeline quality tests, ensuring the Lakehouse is a reliable source of truth for the company's products. • Apply DevOps best practices to ensure the platform infrastructure is provisioned with Terraform.

• Work with team members and architects from other teams to ensure a cohesive Data Platform architecture. • Ensure the Data Platform (Data Lakehouse) architecture is scalable and robust. • Design the taxonomy and structure of the Data Catalog so data is easily discoverable and accessible. • Establish standards (file formats, compression, partitioning strategies) to maximize query efficiency. • Lead the selection of new tools, architectural changes, and features, communicating trade-offs between cost, complexity, and performance to the team. • Design security architecture using AWS Lake Formation to ensure data isolation and auditing. • Implement and manage the data catalog with governance and access controls.

• Define the team’s technical priorities, translating the Head of Data Engineering’s business vision into actionable technical requirements; • Mentorship and Code Review: Raise the team’s technical standard through rigorous code reviews and continuous mentoring of data engineers; • Governance and Standards: Establish Data Quality standards, pipeline observability, and data lineage documentation; • Infrastructure Decisions: Lead the Infrastructure-as-Code strategy (Terraform), ensuring consistent development, staging, and production environments; • Data Culture: Promote a data-driven culture and platform adoption by eliminating manual processes and silos;

• Build and maintain data extraction pipelines from various sources into BigQuery • Refactor and modernize existing extraction systems • Implement monitoring and alerting for pipeline health • Help design and implement our orchestration platform (tooling TBD as a team) • Build scheduling, dependency management, and failure recovery into our data workflows • Create visibility into pipeline status for the broader team and business • Maintain and optimize our BigQuery data warehouse • Support DBT model development and deployment workflows • Implement CI/CD for data infrastructure changes • Support SOC 2 compliance requirements for data systems • Implement security controls and access management in BigQuery • Document data flows and maintain data catalogs