Staff Analytics Engineer — Data Warehouse

Requirements

• Expert SQL: window functions, complex aggregations, query optimization, cost-aware pattern selection, proficiency in Snowflake or equivalent cloud warehouse.
• dbt: deep, production-grade experience — models, tests (singular + generic), docs, snapshots, macros, packages, and incremental strategies. Designed a dbt project from scratch and maintained it in production.
• Airflow / Astronomer: production DAG authoring, backfill handling, reliability patterns, and the Cosmos dbt integration.
• Dimensional modeling: read Kimball (or equivalent), difference between star and snowflake schemas, slowly changing dimensions, fact table grain.
• Stakeholder management: partnering with non-technical stakeholders, driving metric alignment, delivering trusted data products.
• Strong written communication: clear documentation and async updates.

Strong plus

• Experience with financial data or billing data — ARR, usage-based billing, invoice reconciliation, revenue recognition.
• Experience with PII handling, data masking, access-tier modeling, compliance (SOC 2, ISO 27001, GDPR, CCPA).
• Familiarity with lakehouse patterns (Iceberg, Delta, Hudi) and hybrid warehouse/lake architectures.
• Python for data tooling: automation, data quality frameworks, custom dbt macros or operators.
• Experience with Hex, Metabase, or similar notebook/BI tooling.
• Prior experience in high-growth AI/ML infrastructure or platform company.

Responsibilities

Modeling & transformation

• Own and evolve the dbt transformation layer: design, implement, test, document, maintain modular dbt projects for billing, product usage, financial data, operational metrics.
• Build analytics-ready dimensional models following Kimball methodology: star schemas, conformed dimensions, fact tables with right grain, SCD Type 2.
• Design for correctness, performance, cost — partition strategies, incremental models, avoid full-table scans.
• Build and maintain semantic/metrics layer with consistent, auditable metric definitions.

Pipeline orchestration

• Author and maintain Airflow DAGs (Astronomer-managed) for dbt runs, data quality checks, downstream dependencies.
• Solid DAG design: idempotent tasks, backfill strategies, SLA alerting, clean dependency graphs.
• Work in Cosmos (dbt + Airflow) integration — DbtTaskGroup vs custom operator.

Data quality & governance

• Implement data quality checks: freshness, null/uniqueness tests, referential integrity, distribution drift, business-rule assertions.
• Drive data stewardship: ownership, SLAs, source of truth definitions, change communication.
• Handle PII fields: masking, anonymization, access-tier alignment.

Stakeholder management

• Analytical partner to Finance, GTM, Product, Engineering — translate business questions into durable data models.
• Drive alignment on metric definitions, data ownership, delivery tradeoffs.
• Communicate data quality issues, model changes proactively.
• Write clear documentation — model descriptions, column-level lineage, business context.
• Run/contribute to data reviews, data-driven planning, architecture reviews.

Compensation

US base salary range: $240,000 - $275,000 + equity + benefits.