Senior Analytics Engineer

This is a fully remote position, open to applicants in United States.

📋 Description

• Develop and manage the analytical data framework that supports decision-making across Go-to-Market, Product, Finance, People, and Operations.

• Take ownership of the dbt project, ensuring that models are efficient, thoroughly tested, and well-documented.

• Design and oversee the Snowflake data warehouse along with the data ingestion processes.

• Create well-structured core entities and datasets to facilitate complex business operations.

• Construct and manage custom Python/Airflow pipelines that ingest data from third-party APIs into Snowflake.

• Design and implement cross-system reconciliation models to detect discrepancies and safeguard revenue.

• Execute pipeline testing, observability, CI/CD processes, code linting, code reviews, and approvals.

• Standardize metric definitions across various tools and investigate data incidents for remediation.

• Collaborate with teams across Engineering, GTM, Finance, Product, Marketing, RevOps, and People Ops.

• Empower stakeholders with self-service access to reliable insights while promoting data literacy.

• Design and maintain Snowflake Cortex semantic views for AI agents and LLM-driven tools.

• Partner with AI and product teams to define semantic-layer specifications and measurement frameworks.

• Establish experiment designs, attribution models, and success metrics for AI-driven projects.

• Create trusted analytics layers, enhance data quality and reliability, shorten time-to-insight, and address cross-system discrepancies.

⛳️ Requirements

• A minimum of 5 years of experience as an analytics engineer, data engineer, or in a similar position within a SaaS setting.

• In-depth knowledge of SQL, dbt, and contemporary data modeling best practices.

• Proficient in Python for developing pipelines, API integrations, and automating processes.

• Experience in modeling Salesforce data including opportunities, contracts, subscriptions, cases, and field history.

• Proven experience in building custom ELT pipelines that transfer data from third-party APIs into a cloud data warehouse.

• Experience in designing cross-system reconciliation models that involve joining, deduplication, and data comparison.

• Familiarity with event-driven and product usage data tools such as PostHog or Mixpanel.

• Experience linking paid advertising, campaign, and attribution data to product analytics.

• Experience in designing and maintaining governed semantic layers, such as dbt Semantic Layer or Snowflake Cortex.

• Comfortable working with large-scale data systems like Snowflake, BigQuery, or Redshift.

• Strong understanding of CI/CD, Git-based workflows, and automated testing methodologies.

• Proven ability to collaborate effectively with engineers, analysts, and product managers.

• Demonstrated success in utilizing analytics to inform decisions in technical or product-centric settings.

• Comfortable taking ownership of ambiguous challenges and crafting comprehensive solutions.

• Nice to have: Experience with Airflow DAGs and multi-source API ingestion pipelines.

• Nice to have: Knowledge of statistics, A/B testing, significance testing, and incremental-impact measurement.

• Nice to have: Fundamentals of predictive modeling, including classification, feature selection, and model evaluation.

• Nice to have: Understanding of financial SaaS metrics and billing operations.

• Nice to have: Experience in people analytics.

🏝️ Benefits

• Comprehensive health, dental, and vision insurance.

• Competitive salary and performance-based bonuses.

• Flexible work hours and remote work options.

• Opportunities for professional development and continued education.

• Supportive and inclusive work environment.