Senior Analytics Engineer, Chainstack

• 5+ years in analytics engineering, data engineering, BI engineering, or data platform roles with ownership of production analytics systems
• Expert SQL: data modeling, warehouse transformations, query optimization, dependency analysis, and reusable metric and dataset design
• Strong Python: API extraction, automation, data validation, scripts, tests, and production-friendly pipeline code
• Hands-on experience with workflow orchestration such as Airflow, including DAG design, task dependencies, retries, backfills, and operational debugging
• Strong understanding of data quality, lineage, observability, and incident-style response for stale, broken, or inconsistent analytics data
• Strong BI and dashboard skills, preferably Metabase, with ability to design dashboards that are self-explanatory, reliable, and tied to decisions
• Practical understanding of SaaS, product, and business metrics: funnels, activation, TTV, retention and cohorts, churn, revenue, CAC, LTV, and NRR
• Comfortable working with operational data such as availability, incidents, latency, errors, and pipeline health, with ability to model and report it accurately with engineering teams
• AI-native working style: comfortable using AI tools for coding, analysis, documentation, and automation, with strong judgment about validation and production safety
• Strong cross-functional communication: can align technical and non-technical teams on data definitions, tradeoffs, ownership, and what should or should not be measured
• Excellent written and verbal English
• Experience with dbt or similar transformation frameworks

Desirable

• We are looking for a senior analytics engineer to own and evolve our analytics data systems
• In this role, you will build the pipelines, datasets, and dashboards that allow teams across product, engineering, marketing, sales, finance, and support to make informed decisions
• You will work directly with leadership and play a key role in shaping how Chainstack measures and understands its business
• Own analytics data pipelines end to end: design, build, migrate, monitor, and maintain flows across source systems, warehouse, Airflow, SQL transformations, and Metabase-facing datasets
• Build trustworthy self-serve data products: curated tables, views, semantic and metric layers, and Metabase dashboards that teams can use without repeated bespoke analysis
• Implement data quality and observability: freshness checks, reconciliation checks, anomaly alerts, lineage notes, pipeline health dashboards, and clear failure ownership
• Maintain high-value business analytics as the baseline: product activation and TTV, high-potential customer acquisition and retention, revenue and billing analytics, churn signals, marketing funnel analytics, and executive dashboards
• Help teams choose and define important metrics: facilitate KPI definitions, formulas, owners, and decision use-cases without trying to centrally own every team’s metrics
• Translate product and operational changes into analytics requirements: events, properties, logs, source tables, warehouse models, dashboard updates, and validation criteria
• Use AI-native workflows to move faster: apply AI coding assistants, MCPs, automated documentation, query generation, tests, and monitoring helpers while keeping outputs reviewed and production-safe
• Document the analytics system: source-of-truth definitions, data contracts, pipeline architecture, runbooks, dashboard ownership, and known limitations
• Manage unplanned analytics requests pragmatically: triage, fix urgent data issues, and convert recurring requests into durable datasets or dashboards