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United States
Remote only
We'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.
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.
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.
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.
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.
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.
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).
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.