• Architect the Business Insights Foundation • Partner with data engineers to design, build, and maintain analytics transformation layers that act as a single source of truth for downstream reporting, BI, and data science use cases. • Deliver Business Facing Insights • Own the complete analytics lifecycle—from requirements gathering and data modeling to scalable dashboards and self service analytics solutions. • Stakeholder Collaboration • Serve as the technical analytics partner to business stakeholders, translating functional and strategic needs into automated, scalable data solutions. • Data Storytelling • Develop compelling dashboards, metrics, and analytical narratives that communicate insights effectively to both technical and non technical audiences. • Build for Scale & Reusability • Design standardized, reusable business metrics and data models that support automated reporting and enable deep dive self service analysis. • Apply Engineering Best Practices • Improve analytics infrastructure using software engineering principles such as version control, modular design, testing, documentation, and performance optimization.

• Partner with stakeholders across Fullscript to understand recurring data needs, self-serve gaps, and patterns that can be solved through shared tooling and reusable assets • Design and implement self-serve analytics solutions such as trusted datasets, reporting foundations, templates, and workflows that make data easier to access and use independently • Improve how data is surfaced and consumed through BI tools and internal tooling, with a focus on usability, consistency, trust, and adoption • Use SQL, Python, and BI platforms such as Looker to build scalable solutions, including AI-powered analytics tools built internally • Create and deliver training, documentation, office hours, and hands-on enablement sessions that teach teams how to use tools, find data, and analyze it effectively • Partner with Analytics, Data Science, and Data Engineering to strengthen the infrastructure, patterns, and definitions that support self-serve analytics across the business • Own projects end-to-end from discovery through implementation, including ambiguous problems where the right approach isn't obvious

• Working with multiple stakeholders to provide quantitative support. • Define metric rules and logic, deliver tables and reports. • Model business scenarios and solve a wide range of business problems. • Create new pipelines from scratch, merging financial and ERP data sets. • Engineering foundational table resources using Google Cloud Platform (GCP): BigQuery, Dataform, LookML, Dataplex, etc. • Develop pipelines, metrics, tools, and analyses for strategic decision-making.

• Design, build, and maintain robust ETL/ELT pipelines across AWS-native data environments • Own Airflow orchestration — scheduling, dependencies, retries, alerting, and operational support • Develop analytics-ready data models in dbt, using modular, warehouse-first transformation patterns • Work confidently across S3 (raw, staged, curated) and Redshift Serverless for storage and warehousing • Contribute to the roadmap and potential migration toward Snowflake as a future warehouse • Write clean, maintainable Python for pipeline logic, scripting, and lightweight engineering tasks • Embed data quality, testing, and observability into every pipeline — not as an afterthought • Engage directly with client stakeholders: gather requirements, present solutions, and advise on trade-offs • Partner with analysts, product teams, and other engineers in a distributed, remote-first setup