• Lead architecture for reusable, scalable systems • Evolve core abstractions, testing, CI/CD, observability, and cost efficiency • Propose and drive initiatives on behalf of engineering management • Expand and harden integrations with external advertising platforms • Develop reusable query templates and optimize pipelines • Mentor engineers up to L5; set code quality and review standards • Build cross‑team partnerships

• Architect and build the semantic layer powering AI-driven analytics across Abacum.ai, Snowflake Cortex, Sigma, Claude, and the modern analytics tools • Design, build, and refactor DBT models to maintain a trustworthy, evolving single source of truth for enterprise KPIs • Be the go-to analytics partner for Finance, Strategy, and Operations • Bring data to life with compelling presentations and reports that drive key decisions and actions • Raise the bar on how we do analytics engineering — testing, documentation, CI/CD, and performance optimization that the whole team can build on • Evolve our data governance framework — cataloguing, lineage tracking, metric definitions, access controls, and data quality standards

• Structure the data warehouse modeling discipline • Formalize the Credit/Risk domain • Consolidate the Data Warehouse • Scale our semantic layer • Collaborate with Data Engineers

• Build upon and scale our current data infrastructure (Snowflake, dbt, Mode/Metabase) to meet evolving business needs and capture critical business logic. • Work closely with the Senior Analytics Engineering Manager to create and improve data models, data orchestrations, dbt version updates, and development environments. • Contribute expertise and promote best analytics engineering practices in the organization through collaboration with other data teams and stakeholders. • Establish data quality and governance across all business units including, but not limited to, Finance, Accounting Operations, Product, and Engineering