**What Your Day Might Look Like:** - Design, build, and maintain data pipelines that serve models, data, and AI workflows to internal and client-facing applications. - Work across database types — relational, vector, and graph — modelling and storing data appropriately for each access pattern, in close collaboration with data scientists. - Build and maintain dbt models — writing transformation logic, tests, and documentation that ensure data quality and traceability end-to-end. - Operate what you build: instrument pipelines with logging, metrics, and tracing; diagnose and resolve production data issues before they become someone else's problem. - Write clean, tested, production-quality code and contribute to CI/CD pipelines and infrastructure-as-code. - Show up to code reviews, design discussions, and retrospectives — and have something worth saying.

• Architect and build production data pipelines and data platforms that serve models, data, and AI workflows to internal and client-facing applications — and stay accountable for them under live conditions. • Own non-functional quality across your domain: latency and throughput budgets, scalability, reliability, observability, and cost. • Lead the design and operation of multi-model data stores — relational (PostgreSQL, MySQL), vector (Pinecone, Weaviate, pgvector), and graph (Neo4j, Neptune) — applying the right tool to each access pattern, not the most fashionable one. • Set technical direction: write design docs, make build-vs-buy calls, and defend your approach with evidence rather than instinct. • Work across the stack when the problem demands it — services, data access, infrastructure-as-code, CI/CD — and diagnose it when things drift in production. • Raise the floor for the team: mentor mid-level and junior engineers, run rigorous code reviews, and hold the quality bar without making it someone else's job to ask you.

• Design and tune SQL and Snowflake models. • Build and orchestrate engineering pipelines that move data between systems on AWS. • Interrogate data and turn it into Power BI dashboards. • Use modern AI tooling to improve the platform.

• Lead the design, development, and optimization of scalable data platforms and pipelines. • Design, build, and maintain production-grade ETL/ELT workflows for batch and near real-time data processing. • Drive the migration and modernization of data assets from BigQuery and other analytical platforms into Snowflake. • Collaborate with business stakeholders, analysts, and engineering teams to translate business requirements into scalable data solutions. • Implement data quality, validation, monitoring, and observability frameworks.