• 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

• Partner with engineering, operations, program management, and business stakeholders to understand objectives, define reporting requirements, and deliver analytics solutions that support informed decision-making. • Analyze large, complex datasets to identify trends, anomalies, performance gaps, risks, and opportunities for operational improvement. • Conduct data quality assessments, validation, and verification activities to ensure accuracy, completeness, consistency, and integrity across multiple data sources. • Design, implement, automate, and maintain scalable data collection, transformation, validation, and reporting processes. • Develop and maintain dashboards, KPI scorecards, reports, and self-service analytics solutions that provide visibility into operational performance, program health, technical metrics, and business outcomes. • Build data validation frameworks, monitoring processes, and quality controls to proactively identify and resolve data issues. • Collaborate with technical teams to enhance data models, reporting structures, metadata management practices, and overall data governance initiatives. • Translate complex technical findings into clear, concise, and actionable recommendations for both technical and executive audiences. • Drive process improvements through automation, standardization, and implementation of scalable analytics and reporting solutions. • Support root cause investigations by leveraging data to identify drivers of operational issues, risks, and performance challenges. • Document methodologies, business requirements, reporting processes, and data quality standards to ensure consistency and scalability.