Chainstack 🛠️💙

• Own the strategy, technical direction, delivery, and production quality of data infrastructure features from discovery to rollout. • Understand user pain points around blockchain data access, latency, correctness, historical coverage, reliability, and operational visibility. • Translate product and platform needs into clear specs, epics, user stories, acceptance criteria, implementation slices, and rollout plans. • Build backend and data systems in Go and/or Rust, and work across databases, queues, object storage, observability, CI/CD, and infrastructure when needed. • Design and improve indexing, caching, validation, benchmarking, and repair workflows for large-scale blockchain data systems. • Use AI-first development practices to accelerate research, specification, implementation, testing, and documentation while maintaining a high verification bar. • Collaborate with product, infrastructure, support, and customer-facing teams to gather context, make trade-offs, and close the loop after release. • Define and analyze technical and product metrics such as latency, throughput, accuracy, freshness, coverage, error rate, reliability, and operational cost. • Create internal tools, benchmarks, dashboards, alerts, and runbooks that make system behavior observable and easier to operate. • Review code, specs, and designs with focus on correctness, simplicity, maintainability, performance, and production ownership. • Conduct technical research and competitor analysis to identify opportunities for improving data products and infrastructure.

• Gain a deep understanding of our technology, value proposition, and target markets, embracing our proven sales methodology for winning. • Use active listening and consultative sales skills to understand our prospects and position how Chainstack can uniquely solve their problems. • Effectively convey how Chainstack can solve each prospect’s specific problems. • Positively contribute to the Chainstack culture, working alongside colleagues to forge a strong team-based sales approach.

• 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.

• Lead a team of engineers building a platform to manage and scale blockchain infrastructure. • Oversee systems that handle authentication, rate limiting, request routing, and data access across multiple blockchain networks. • Design off-chain data storage solutions and APIs for internal and external consumers. • Collaborate cross-functionally to align engineering output with product and infrastructure goals. • Mentor and support team members through regular 1:1s, technical feedback, and growth planning. • Participate in hiring and shaping team culture. • Balance leadership and technical contributions—whether hands-on or in a guiding capacity.