Role Description We are looking for a Data & AI Systems Engineer who can proficiently: - Architect and engineer messy data. - Write code leveraging AI. - Engineer agentic workflows. - Develop software. This role is not: A pure software engineer who happens to work with data. The core challenge is making unreliable data sources trustworthy enough to drive business decisions. Candidates who have only worked with clean, well-structured data will struggle here. Responsibilities: - Design and build the data platform: ingestion, transformation, storage, and serving. - Integrate many heterogeneous data sources with varying formats, quality levels, and update frequencies. - Own the data quality framework: validation, lineage tracking, monitoring, and alerting. - Leverage AI coding agents to accelerate development and infrastructure management. - Document architecture decisions in lightweight ADRs (architecture decision records). - Document data source mappings and transformation logic alongside the code. - Generate insight and visualization for non-technical users of data. Qualifications - Ability to quickly assimilate and understand industry data nuance. - Senior+ software engineer with strong architectural judgment. - Demonstrated experience integrating messy, real-world data from multiple sources — not just clean API-to-API pipelines. - Familiarity with data quality tooling (e.g., Great Expectations, dbt tests, Soda, or equivalent). - Strong experience using AI coding agents to deliver production-quality projects and extend them over time. - Comfort operating as the sole technical builder, at least in the initial phase. Requirements - A strong cultural fit marked by ownership, humility, initiative, and collaboration. - A self-starting mindset with the ability to move work forward without constant direction. - A bias toward action, learning, and improvement in fast-paced environments. - Clear and professional communication skills, both verbal and written. - Strong organizational skills and the ability to manage multiple priorities effectively. Benefits - Salary: $155k - $175k based on experience. - PTO. - Health stipend.

• Design, build, and own the end-to-end GTM data model, including lifecycle stages, funnel metrics, attribution logic, forecasting signals, scoring inputs, and cross-sell indicators. • Create and maintain a semantic layer that ensures consistent, trusted reporting across all GTM teams. • Define GTM metrics, data standards, and analytical logic that unify Sales, Marketing, Customer Success, Partnerships, and Product GTM data. • Partner with business and GTM leadership to translate strategic questions into scalable analytical models. • Build and maintain BI dashboards and analyses (e.g., pipeline, conversion, campaign performance, onboarding, and cross-sell). • Provide data logic that powers routing, scoring, enrichment, lifecycle automation, and GTM workflows. • Partner closely with Systems and Operations teams to ensure workflows run on clean, validated, and battle-tested data models. • Establish data validation, QA processes, and monitoring to maintain high data quality across systems. • Document data models, definitions, and logic to ensure clarity, consistency, and usability across stakeholders.

• Build and maintain the Regulatory Data Mart • Enable compliance stakeholders to self-serve regulatory data needs • Build AI-driven workflows for regulatory reporting • Ensure compliance with regulatory reporting SLAs

• Building Data Models: Create complex dbt models, custom macros, and reusable packages. Optimise transformations and implement robust testing strategies to ensure data integrity and model performance. • Ownership: Monitoring and maintaining dbt workflow jobs, ensuring smooth data refreshes and up-to-date pipelines. You will also be responsible for data models for BI analytics & company level reporting. • Ensuring Data Accuracy: Writing tests and assertions to validate data integrity and consistency across models. • Documenting and Standardizing: Creating and maintaining thorough documentation of dbt processes to ensure best practices within the BI team. • Translating Complex Data Concepts: Acting as a key communicator, translating technical data issues into understandable business terms for stakeholders. • Mentoring Team Members: Supporting junior analysts and data engineers, especially in setting up experimentation platforms and data best practices. • Collaborating Across Teams: Working closely with the product, engineering, and BI teams to ensure data infrastructure supports evolving business needs.