Senior Analytics Engineer

Responsibilities

Build & Own the Data Foundation

• Own and evolve the dbt project, ensuring models are performant, well-tested, and documented.
• Design and maintain the Snowflake data warehouse and ingestion processes.
• Create core entities and datasets using modern data-modeling practices for complex business processes and logic.
• Build and maintain custom Python/Airflow pipelines to ingest third-party APIs into Snowflake.
• Design and operate cross-system reconciliation models to compare source-system data, surface discrepancies, and protect revenue.

Drive Data Quality & Automation

• Implement testing and observability for analytics pipelines.
• Enforce CI/CD practices including automation, linting, testing, code review, and approvals.
• Standardize metric definitions across tools.
• Investigate and document data incidents from root-cause analysis through remediation tracking and stakeholder communication.

Cross-Functional Collaboration

• Act as a data liaison between Engineering, GTM, and Finance to ensure consistent metric definitions and proper system instrumentation.
• Enable stakeholder self-service access to trusted insights.
• Promote data literacy and coach stakeholders on querying, dashboarding, and metric interpretation.

Build AI-Ready Data Infrastructure

• Design and maintain Snowflake Cortex semantic views as governed data interfaces for AI agents and LLM-powered tools.
• Partner with AI and product teams to scope, build, and validate semantic-layer definitions for internal AI assistants.
• Build measurement frameworks for AI-powered initiatives, including experiment design and attribution modeling.

Requirements

• 5+ years of experience as an analytics engineer, data engineer, or similar role in a SaaS environment.
• Deep expertise in SQL, dbt, and modern data modeling.
• Proficiency in Python for pipeline development, API integrations, and automation.
• Experience modeling Salesforce data, including opportunities, contracts, subscriptions, cases, and field history.
• Experience building custom ELT pipelines that ingest third-party APIs into a cloud data warehouse.
• Experience designing cross-system reconciliation models involving joins, deduplication, and source-data comparisons.
• Experience with event-based and product-usage data such as PostHog or Mixpanel.
• Experience connecting marketing data, including paid ads, campaigns, and attribution, to product analytics.
• Experience designing and maintaining governed semantic layers, such as dbt Semantic Layer or Snowflake Cortex.
• Familiarity with large-scale data systems including Snowflake, BigQuery, or Redshift.
• Strong familiarity with CI/CD, Git-based workflows, and automated testing.
• Experience collaborating with engineers, analysts, and product managers.
• Demonstrated success using analytics to drive decisions in technical or product-focused environments.
• Comfort taking ownership of ambiguous problems and designing end-to-end solutions.

Nice-to-Haves

• Experience building and maintaining Airflow DAGs and orchestrating multi-source API ingestion pipelines.
• Strong foundation in statistics and experiment design, including A/B testing, significance testing, and incremental-impact measurement.
• Experience with predictive-modeling fundamentals, including classification, feature selection, and model evaluation.
• Familiarity with financial SaaS metrics and billing operations, including ARR, MRR, NRR, subscription reconciliation, and revenue recognition.
• Experience with people analytics, including headcount, attrition, and compensation benchmarking.

Compensation

• Annual salary range: $150,000–$190,000.