Senior Analytics Engineer - US (Remote)

• 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.

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).

• 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.