• Architect a greenfield, multi-layer data warehouse (raw, refined, serving) that separates analytical workloads from production OLTP traffic. • Deliver a governed, self-service data-access layer for internal consumers first (Product, CSM, Deployment/Operations, and Leadership) as Phase 1, ahead of customer-facing conversational analytics. • Build a semantic and metrics layer so every metric, such as "scan accuracy by site," is defined once in code and stays identical across every dashboard and product, making self-service safe from metric drift. • Own the quality bar: 99%+ availability SLA with freshness guarantees, 100% traceability, zero cross-tenant leakage, 99.5%+ pipeline success, and no data loss.  • Design tenant isolation, per-tenant cost attribution, and schema and row-level RBAC to scale toward hundreds of tenants (300+ target), not today's fleet size. • Own data-ingestion correctness at the boundary with the integration/backend team, covering data contracts, schema validation, and pipeline quality, so WMS data lands in the right place, shape, and time across WMS versions. • Stand up a data catalog and lineage layer (Purview as the Azure-native fit, DataHub as the open-source alternative) so every consumer can find data, see ownership, and trace lineage when a metric looks wrong. • Prove the foundation end to end on Gather's drone product, then generalize it so each new product extends the model instead of rebuilding it • Act as the connective tissue between product and ML (3DCC, damage detection). Link structured records to unstructured drone imagery and video with full traceability, and stand up the data-infra readiness for feature stores and annotation pipelines on one trusted foundation.

• Define company data assets (data model) • Define and design data integrations, data quality frameworks and design and evaluate open source/vendor tools for data lineage • Work closely with Dropbox business units and engineering teams to develop strategy for long term Data Platform architecture to be efficient, reliable and scalable • Conceptualize and own the data architecture for multiple large-scale projects, while evaluating design and operational cost-benefit tradeoffs within systems • Collaborate with engineers, product managers, and data scientists to understand data needs, representing key data insights in a meaningful way • Design, build, and launch collections of sophisticated data models and visualizations that support multiple use cases across different products or domains • Optimize pipelines, dashboards, frameworks, and systems to facilitate easier development of data artifacts

• Lead a team of 2-3 engineers • Working with fellow Nava engineers to design, review, and build well-crafted software • Working in an agile manner to efficiently ship new features that meet user needs • Design and develop scalable data ingestion and processing pipelines • Implement large-scale data ecosystems within cloud-based platforms that include data management and data governance of structured and unstructured data • Implement data validation and quality checks to ensure accuracy and consistency • Work with cross-functional project teams to gather business requirements and translate to detailed technical specifications • Work with Government partners to assist and develop data engineering applications and pipelines that will enable data services and processing capabilities • Participate in software design and code reviews • Maintain security and privacy standards in all aspects of the data pipeline • Taking part in hiring activities (e.g., submitting referrals, conducting interviews, and attending interview debriefs), as needed

• Support entire data stack from ingestion through warehousing and transformation • Build and maintain bespoke syncs for operational tools • Investigate data quality issues • Conduct team syncs for alignment