• Design, build, and own end-to-end data solutions for Core products. • Establish and scale analytics foundations by implementing data pipelines. • Oversee the design and development of interactive analytical experiences. • Mentor and support the growth of junior team members through coaching.

• drive significant business impact by shaping our data infrastructure • developing robust data models • promoting best practices in analytics engineering • collaborate closely with the Marketing team • ensure that funnels are being tracked and surfaced in our data models • evaluate Marketing campaigns using accurate data • create trusted, self-serve datasets • proactively identify and resolve data quality issues • add comprehensive metadata to datasets • partner with Marketing stakeholders to translate business questions into robust data products • enable Marketing teams to confidently use Omni and core GTM datasets • activate data from Snowflake into operational tools

• Build and operate the data pipelines that feed Platform Analytics — event ingestion and entity/reference ingestion into Snowflake — to a high standard of reliability and quality. • Build and operate the traditional data-engineering ELT to create a reliable source of truth in the data warehouse. • Contribute to the data models, curating and modeling source-system data into trusted, conformed datasets and applying dimensional modeling and engineering best practices. • Support production performance, reliability, and cost — performance tuning, monitoring and alerting, and resource management. • Help evolve the platform toward its target-state architecture, implementing the design and providing architectural and scale recommendations. • Lead the development lifecycle for your work — implementation, testing, and deployment — collaborating with the team to deliver and maintain the platform. • Help mentor junior engineers and contribute to team standards for code quality and ways of working. • Use AI coding tools productively in daily engineering work — accelerating development, testing, and debugging.

• Partner with product, go-to-market, and executive stakeholders — running discovery on ambiguous questions and scoping the metrics and data they actually need • Raise data trust — adding the validation, definitions, and documentation that let users rely on the numbers and our tooling • Expand and own our semantic / metrics layer — defining and maintaining metric definitions and models so analytics are consistent, trustworthy and reusable across the company • Deliver self-serve and AI-accessible analytics — curated datasets, metrics, and reporting that internal partners and our agentic / LLM querying surface can answer on their own • Ingest net new data — designing and building pipelines to bring in new sources such as GTM and product-usage data and modeling them for analytics