The RoleWe're looking for a Senior Analytics Engineer to build and scale the analytical foundation that powers decision-making across Go-to-Market, Product, Finance, People, and Operations teams.
You will sit at the intersection of data engineering and analytics: transforming raw product, marketing, financial, and operational data into clean, well-modeled, and trustworthy datasets. Your work will power everything from executive dashboards and cohort analyses to experimentation, billing operations, AI-powered outreach, and semantic layers that let AI agents answer stakeholder questions autonomously.
This is a highly cross-functional role - you'll partner closely with Product Management, Marketing, RevOps, Finance, People Ops, and Engineering to ensure our analytics stack is robust, scalable, and aligned with the business.
ResponsibilitiesBuild & Own the Data Foundation- Own and evolve our dbt project - ensuring models are performant, well-tested, and documented.
- Design and maintain the Snowflake data warehouse and ingestion processes.
- Use modern data modeling best practices to create core entities and datasets that account for complex business processes and logic.
- Build and maintain custom Python/Airflow pipelines to ingest data from third-party APIs into Snowflake.
- Design and operate cross-system reconciliation models that compare data across source systems to surface discrepancies and protect revenue.
Drive Data Quality & Automation- Implement testing and observability for analytics pipelines.
- Enforce CI/CD best practices, such as automation, linting, tests, code review and approvals.
- Standardize metric definitions and ensure they are consistently computed across tools.
- Investigate and document data incidents end-to-end - from root cause analysis through remediation tracking and stakeholder communication.
Cross-Functional Collaboration- Act as data liaison between Engineering, GTM, and Finance - ensuring consistent metric definitions and proper system instrumentation.
- Enable stakeholder self-service access to trusted insights.
- Drive data literacy: evangelize best practices in querying, dashboarding, and interpreting metrics; coach stakeholders toward self-serve.
Build AI-Ready Data Infrastructure- Design and maintain Snowflake Cortex semantic views that serve as the governed data interface for AI agents and LLM-powered tools.
- Partner with AI/product teams to scope, build, and validate the semantic layer definitions that power internal AI assistants.
- Build measurement frameworks for AI-powered initiatives - including experiment design and attribution modeling.
QualificationsMust Have:- 5+ years of experience as an analytics engineer, data engineer, or a similar role in a SaaS environment.
- Deep expertise in SQL, dbt, and modern data modeling best practices.
- Proficiency in Python for pipeline development, API integrations, and automation.
- Experience modeling Salesforce data - opportunities, contracts, subscriptions, cases, and field history.
- Proven experience building custom ELT pipelines that ingest data from third-party APIs into a cloud data warehouse.
- Experience designing cross-system reconciliation models - joining, deduplicating, and comparing data across multiple source systems to surface discrepancies.
- Proven experience working with event-based and product usage data (e.g., Posthog, Mixpanel).
- Experience connecting marketing data (paid ads, campaigns, attribution) to product analytics - ideally having built end-to-end pipelines from ad platforms through to conversion and retention metrics.
- Experience designing and maintaining semantic layers that serve as governed data interfaces (dbt Semantic Layer, Snowflake Cortex, or similar).
- Comfortable with large-scale data systems (Snowflake, BigQuery, Redshift).
- Strong familiarity with CI/CD, Git-based workflows, and automated testing.
- Experience collaborating cross-functionally with engineers, analysts, and product managers.
- Demonstrated success using analytics to drive decisions in a technical or product-focused environment.
- Comfort taking ownership of ambiguous problems and designing end-to-end solutions.
Nice to Have:- Experience building and maintaining Airflow DAGs and orchestrating multi-source API ingestion pipelines.
- Strong foundation in statistics and experiment design - A/B testing, significance testing, and measuring incremental impact.
- Experience with predictive modeling fundamentals - classification, feature selection, and model evaluation.
- Familiarity with financial SaaS metrics and billing operations (ARR/MRR/NRR, subscription reconciliation, revenue recognition).
- Experience with people analytics (headcount, attrition, compensation benchmarking).
What Success Looks Like- Establish a trusted, well-modeled analytics layer that product managers, marketers, and leaders rely on daily.
- Improve data quality and reliability, with clear SLAs and observability around our most critical models.
- Drive down time-to-insight by enabling self-serve access to high-quality datasets and metrics.
- Extreme ownership over critical infrastructure and data models that directly impact product decisions and business growth.
- Partner with data engineers and analysts to build a semantic layer that AI agents can use to answer stakeholder questions - and actively maintain the semantic views that power those agents.
- Proactively identify and quantify data discrepancies across systems and drive them to resolution with operational teams.
- Design measurement frameworks for new initiatives - defining what to track, how to measure impact, and what "success" means before launch.
