Senior Analytics Engineer

About the role

You own the integrity of analytics from raw ingestion to trusted business metrics. You design and operate scalable data platforms in Snowflake, using dbt and Python to turn complex requirements into performant, observable pipelines. You collaborate closely with Engineering, GTM, and Finance to align metric definitions, drive data literacy, and enable self-service decision making across the business.

Key facts

What you'll do

• Conduct risk assessments and design robust data models and dbt projects so that analytics are accurate, performant, well-tested, and documented.
• Architect and maintain a scalable Snowflake data warehouse and ingestion processes that reliably support cross-system comparisons.
• Build core entities and datasets following modern data modeling best practices to create a clear, consistent semantic foundation.
• Develop and maintain custom Python and Airflow pipelines that automate ingestion from third-party APIs into Snowflake while ensuring reliability and observability.
• Design and operate cross-system reconciliation models that join, deduplicate, and compare data from multiple sources, then surface discrepancies for remediation.
• Implement comprehensive testing and observability for analytics pipelines, including automated checks that enforce data quality and lineage.
• Enforce CI/CD best practices such as automation, linting, tests, code review, and approvals to keep analytics pipelines maintainable and secure.
• Standardize metric definitions and computation logic so that key metrics remain consistent across tools and teams.
• Investigate data incidents end-to-end, performing root cause analysis, tracking remediation, and communicating status to stakeholders.
• Act as a data liaison between Engineering, GTM, and Finance to ensure proper system instrumentation and consistent metric definitions.
• Enable stakeholder self-service access to trusted insights by building clear data structures and documentation.
• Drive data literacy by evangelizing best practices in querying, dashboarding, and interpreting metrics, coaching stakeholders toward self-serve analysis.
• Design and maintain Snowflake Cortex semantic views for AI agents and LLM-powered tools, ensuring the semantic layer supports AI use cases.
• Partner with AI and product teams to scope, build, and validate semantic layer definitions for AI-driven initiatives.
• Build measurement frameworks for AI-powered programs, including experiment design and attribution modeling to evaluate impact.

Requirements

• Bring 5 or more years of experience as an analytics engineer, data engineer, or similar role in a SaaS environment.
• Demonstrate deep expertise in SQL, dbt, and modern data modeling best practices.
• Show proficiency in Python for pipeline development, API integrations, and automation.
• Provide evidence of modeling Salesforce data, including opportunities, contracts, subscriptions, cases, and field history.
• Present proven experience building custom ELT pipelines that ingest data from third-party APIs into a cloud data warehouse.
• Offer experience designing cross-system reconciliation models that join, deduplicate, and compare data across multiple source systems.
• Have experience working with event-based and product usage data from platforms such as Posthog and Mixpanel.
• Have experience connecting marketing data from paid ads and campaigns through conversion and retention metrics, building end-to-end pipelines from ad platforms to product analytics.
• Show experience designing and maintaining semantic layers, such as dbt Semantic Layer or Snowflake Cortex, or similar tools.
• Be comfortable operating large-scale data systems such as Snowflake, BigQuery, or Redshift.
• Demonstrate strong familiarity with CI/CD, Git-based workflows, and automated testing.
• Illustrate a track record of collaborating cross-functionally with engineers, analysts, and product managers.
• Demonstrate success using analytics to drive decisions in a technical or product-focused environment.
• Be comfortable taking ownership of ambiguous problems and designing end-to-end solutions.

Nice to have

• Have experience building and maintaining Airflow DAGs and orchestrating multi-source API ingestion pipelines.
• Hold a strong foundation in statistics and experiment design, including A/B testing, significance testing, and measuring incremental impact.
• Show experience with predictive modeling fundamentals such as classification, feature selection, and model evaluation.
• Possess familiarity with financial SaaS metrics and billing operations, including ARR, MRR, NRR, subscription reconciliation, and revenue recognition.
• Have experience with people analytics, including headcount, attrition, and compensation benchmarking.

Engineering methods

• Apply SQL, dbt, Python, Airflow, Snowflake, Salesforce, Posthog, Mixpanel, CI/CD, Git, semantic layer, and Snowflake Cortex in your day-to-day work.

Relevant systems

• Snowflake, dbt, Airflow, Python, Salesforce, Posthog, Mixpanel, Semantic layer.

Practical notes

• No preferred degrees or exact salary bands are specified for this role;