Senior Analytics Engineer

Are you a data-driven engineer looking to build reliable analytics systems that power real business decisions? Join Chainstack and help shape the data foundation behind Web3 infrastructure.

Chainstack powers global blockchain applications across fintech, DeFi, wallets, custodians, analytics, and everything in between.

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.

• 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.

• Web3 or blockchain domain familiarity.
• Experience with CDPs and marketing analytics stacks, such as Segment.
• Experience with observability systems such as Prometheus, Grafana, or ELK.
• Experience with dbt or similar transformation frameworks.
• Experience building lightweight internal tools or automation around data workflows.