• Design the high‑level and detailed architecture blueprint for Irth’s new Databricks‑based data estate, including lakehouse patterns, medallion architecture (Bronze/Silver/Gold), and semantic layers. • Develop a multi‑cloud ingestion and consolidation strategy for data sourced from AWS (S3, RDS, DynamoDB), Azure (SQL MI, Storage Accounts), GCP, and on‑prem systems. • Define patterns for data lineage, metadata management, data quality, CDC/SCD, streaming and batch ingestion, and federated query capabilities. • Establish an enterprise‑level data governance model, including classification schemas, residency rules, cataloging (Unity Catalog), retention policies, and security standards. • Partner with security and compliance teams to enforce GDPR, PIPEDA, Australian Privacy Act, SOC 2, and other regulatory requirements. • Architect for scalability, reliability, cost‑efficiency, and support of downstream analytics, reporting, GIS workflows, and AI/ML workloads. • Produce architectural documentation, diagrams, runbooks, and design standards / best practices. • Collaborate closely with a distributed team of data engineers to implement ingestion pipelines, Delta Lake storage patterns, orchestration workflows, and Databricks Lakeflow components. • Provide technical leadership, code reviews, architectural guidance, and hands‑on support when needed. • Ensure architecture is implemented consistently across tenants, clouds, products, and engineering teams. • Work with DevOps/Platform teams on infrastructure, identity, networking, VPC/VNet, and CI/CD integration. • Establish monitoring, observability, and operational governance for the data estate. • Define and enforce RBAC/ABAC policies, encryption standards, audit logging, and tenant‑level isolation. • Partner with governance teams to unify metadata and lineage across all sources via Unity Catalog and Purview integration. • Ensure ongoing compliance with data residency and cloud‑specific regulatory constraints. • Develop standards for retention, archival, disaster recovery, and cross‑region protections. • Architect support for operational reporting, BI (Power BI, Databricks SQL), semantic modeling, and consumption patterns. • Design foundations enabling AI/ML use cases such as predictive risk modeling, summarization, geospatial analytics, and LLM‑driven insights. • Advance a BI‑tool‑agnostic semantic model strategy for consistent business definitions across products. • Serve as Irth’s senior expert on Databricks Lakehouse architecture, modern data engineering, and multi‑cloud data strategy. • Mentor engineers and analysts in best practices for data modeling, governance, pipeline design, and scalable architectures. • Partner with product teams, executives, and stakeholders to translate business requirements into technical data solutions. • Participate in vendor evaluations, roadmap planning, and architectural governance boards.

• Collaborate with data scientists, analysts, and other stakeholders to understand data requirements and deliver tailored data solutions. • Develop efficient data processing and transformation workflows to support analytics and reporting needs. • Design, build, and maintain data pipelines and automated extract, transform, load (ETL) processes using tools like Python, R, and platform‑specific environments such as Jupyter Notebooks. • Integrate disparate structured and unstructured data from APIs, databases, and cloud storage into unified datasets utilizing ETL patterns, frameworks, query techniques. • Implement processes for data cleaning, transformation, and validation to ensure data accuracy, consistency, and compliance with security and privacy policies. • Develop dashboards, visualizations, and analytical products leveraging QuickSight or other mission‑approved tools to support operational decision‑making. • Provide full lifecycle assistance in deploying, optimizing, maintaining complex code with data processing routines running in development, test, and production • Optimize code through advanced algorithmic concepts to facilitate more efficient

• Design, build, and maintain ETL/ELT pipelines and integrations across legacy and cloud systems. • Model, store, and transform data to support analytics, reporting, and downstream applications. • Build API-based and file-based integrations across enterprise platforms. • Ensure compliance with federal security and data governance frameworks (HIPAA, NIST, RMF, CMMC). • Implement monitoring, logging, and data quality checks for reliable, scalable pipelines. • Partner with analysts and data science teams to deliver trusted, AI-ready datasets. • Support data migration and integration initiatives across enterprise systems. • Maintain schema management, data lineage, and documentation to support audit and traceability. • Troubleshoot ingestion, pipeline, and data quality issues.

• Lead scalable data engineering efforts that empower cross-functional teams with reliable, timely, and actionable data • Build and optimize scalable, efficient ETL and data lake processes that proactively catch issues before they impact the business • Own the ingestion, modeling, and transformation of structured and unstructured data to support reporting and analysis across all business units • Partner closely with BI and Analytics to deliver clean, query-ready datasets • Maintain and enhance database monitoring, anomaly detection, and quality assurance workflows • Drive adoption and advancement of Looker dashboards by ensuring robust and scalable backend data support • Contribute to the future of Launch Potato’s data team by mentoring peers and shaping a high-performance, quality-first engineering culture