Director, Business Systems & Platform Engineering

The Engineering and Business Systems team collaboratively works across Chainalysis’ Business Automation, Data and Systems organization to ensure core business systems and platforms enable business policies and operational processes. Their mission is to empower employees by building foundational systems and data products that drive business decisions and processes while accelerating Chainalysis’ growth.

Key Responsibilities:

• Lead the systems and engineering organization through a shift from a traditional IT and admin-centric model to one centered around platform and data engineering.
• Own the technical architecture, design, and delivery roadmap across business systems, enterprise data, and integrations.
• Directly manage and grow systems and engineering-track managers and direct reports, including technical mentorship and career pathing.
• Collaborate with team members to establish and codify standards, workflows, and engineering practices into a coherent operating model leveraging AI and agentic processes.
• Partner with Product, Success, and Program Management to prioritize and deliver the BADaS roadmap by translating business and product requirements into technical scope, staffing, and sequencing decisions.
• Act as the internal technical counterpart to vendors and partners, including reviewing architecture proposals, approving technical designs, and owning technical builds.
• Ensure security, RBAC, and data-governance posture of BADaS platforms, especially from an audit and access-control risk perspective.
• Partner with engineering leaders across Chainalysis on best practices and leverage existing platforms, components, solutions, and codebases.

Required Qualifications:

• 8+ years of experience in software, platform, or data engineering, including 4+ years managing engineers and engineering managers.
• Strong organizational leadership in hiring, career pathing, and building succession depth across a multi-manager organization with real single-point-of-failure risk.
• Comfortable operating in an ambiguous, transformation-in-progress environment.
• Demonstrated ownership of team-based technical architecture and engineering practices, including repo management, CI/CD, code review standards, and test/regression discipline.
• Experience managing a technically mixed organization (engineers, data engineers, and platform/systems administrators) and raising the technical bar of admin-facing work.
• Data-first orientation focused on data architecture and flow.
• Strong working fluency in modern, event-based, service-oriented architectures using server-side JavaScript/TypeScript/Node.js within a primarily serverless AWS environment.
• Deep experience with REST/GraphQL API architectures, including OAuth and JWT authorization strategies.
• Familiarity with data engineering fundamentals and infrastructure, including SQL, Python, dbt/data modeling, pipeline orchestration, batch vs near-real-time ETLs, and medallion architectures.
• Experience with IaC platforms (e.g., Terraform) for DevOps and cloud infrastructure.

Nice to Have:

• Experience with modern AI-centric engineering workflows and practices.
• Model Context Protocol (MCP) and building autonomous AI agents.
• Hands-on experience with graph theory and databases, especially as a context layer for AI co-pilots, workflows, and agents.
• Track record of managing vendors and implementation partners on complex platform builds, including technical design review and sign-off authority.
• Experience with enterprise SaaS platforms used in GTM and/or Finance (e.g., Marketo, Salesforce, NetSuite, Nue, Salesforce Revenue Cloud Advanced, Conga, DealHub) at an architectural and API level.

Technologies Used:

• Python, Node.js
• AWS (Lambda, Step Functions, S3, CloudWatch, VPCs, IAM)
• Workato, Fivetran
• Databricks, Airbyte
• Terraform
• Git, Github
• Salesforce, Netsuite, Workday, Marketo, Clari, Adaptive
• LiteLLM, Claude Code

AI at Chainalysis:

AI is not just a feature at Chainalysis—it’s a new way of working. AI transforms instructions into actionable work, helping the company move faster than the threats it counters. Employees are expected to take ownership of AI outputs and ensure quality. Chainalysis leverages proprietary data, regulatory relationships, and crypto expertise to shape the next era of AI-driven intelligence, with every employee contributing to this transformation.