Job DescriptionAbout the Opportunity\\n
We are building an AI-enabled intelligence platform that helps enterprise and public-sector organizations detect, assess, and respond to real-world risks more effectively. Our technology brings together complex data sources, advanced analytics, and modern AI capabilities to support security, risk, and operational decision-making.
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The company operates across both commercial and government-facing environments. As a result, the engineering organization must move with the urgency of a high-growth software company while maintaining the security, reliability, and compliance discipline expected by sophisticated enterprise and public-sector customers.
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We are an early-stage, well-funded company entering a significant period of growth. This role will be central to defining how engineering operates, scales, and delivers-not merely managing an existing function.
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The Role\\n
We are seeking a Principal Software Engineer/Tech Lead to lead the technical direction, execution, and growth of the engineering organization.
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This is a player-coach position for someone who is equally comfortable making consequential architectural decisions, improving delivery execution, developing engineers, and working directly with executive leadership. You will remain close enough to the technology to resolve difficult problems and establish sound technical direction, while also bringing the operational rigor needed to make engineering delivery predictable as the organization grows.
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Reporting directly to the CEO, you will be among the company's most senior technical leaders. You will partner closely with Product leadership, which owns product strategy, roadmap prioritization, and the broader product-and-engineering operating rhythm. Together, you will ensure the team can translate priorities into high-quality, dependable product delivery across commercial and public-sector use cases.
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What You'll Do\\n
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AI-Enabled Engineering and Applied AI\\n
\\n - Bring practical experience with modern AI-assisted development workflows, including coding assistants, agentic development tools, LLM-powered internal tooling, and automated testing or documentation workflows.
\\n - Identify responsible opportunities to use AI to improve engineering productivity, development quality, testing coverage, internal operations, and time to delivery.
\\n - Establish sensible guardrails for AI use, especially where source code, sensitive customer data, regulated data, or government-related requirements are involved.
\\n - Partner with Product and applied AI or data-science teams on product-level AI decisions, including model selection, evaluation frameworks, reliability, observability, cost management, and appropriate human oversight.
\\n - Help shape a durable point of view on how AI changes engineering workflows, team composition, hiring, and operating models.
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Data Architecture and Platform Direction\\n
\\n - Define and evolve the company's long-term data architecture across intelligence, analytics, and operational data pipelines.
\\n - Design systems that can scale across growing data volumes, new data modalities, increasing customer requirements, and more stringent security or regulatory expectations.
\\n - Make high-leverage platform decisions across data storage, ingestion and transformation pipelines, data modeling, databases, warehousing, observability, and ML/AI operations.
\\n - Balance development speed, infrastructure cost, product flexibility, security, and customer-specific deployment needs.
\\n - Establish sound approaches to data isolation, sensitive-data handling, access controls, governance, lineage, retention, and security-by-design.
\\n - Partner with Product, Security, and Governance/Risk/Compliance stakeholders to ensure the platform matures responsibly as the business expands.
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Engineering Execution and Delivery\\n
\\n - Build a pragmatic engineering operating model that improves execution without creating unnecessary process.
\\n - Establish clear mechanisms for roadmap planning, capacity planning, technical-debt prioritization, sprint and release management, dependency tracking, and delivery-risk identification.
\\n - Improve visibility into engineering commitments, delivery timelines, release readiness, and tradeoffs.
\\n - Create a culture of predictable execution so company leadership can rely on engineering forecasts and understand when priorities, scope, staffing, or sequencing need to change.
\\n - Lead cross-functional planning with Product, customer-facing teams, and company leadership to align technical delivery with enterprise implementation needs and public-sector sales cycles.
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Technical Leadership\\n
\\n - Remain hands-on in the areas where senior technical judgment has the greatest impact, including architecture reviews, difficult technical decisions, code and design review, incident analysis, and removing execution blockers.
\\n - Set high standards for software quality, testing, documentation, release practices, reliability, and security.
\\n - Guide the evolution of the company's architecture and technical stack as product needs, scale, and customer requirements change.
\\n - Develop a disciplined approach to technical debt, ensuring the organization can balance near-term product delivery with long-term platform health.
\\n - Help create an engineering culture that values thoughtful decision-making, practical solutions, ownership, and continuous improvement.
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Team Leadership and Talent Development\\n
\\n - Lead, coach, and grow a multidisciplinary engineering organization that may include software engineers, data engineers, machine-learning engineers, data analysts, and infrastructure or DevOps talent.
\\n - Own hiring strategy, interviewing, onboarding, performance management, career development, and team design as the organization scales.
\\n - Foster a high-trust, low-ego environment with clear accountability and strong collaboration across technical and nontechnical teams.
\\n - Translate business context into clear engineering priorities, while helping executive stakeholders understand technical constraints, risks, investment needs, and delivery tradeoffs.
\\n - Build management practices that support both individual growth and consistently high team performance.
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What We're Looking For\\n
\\n - 10+ years of progressive experience across software engineering, platform or data architecture, and engineering leadership.
\\n - Demonstrated ownership of architecture at scale, particularly data-intensive or platform-oriented systems. You have designed core infrastructure and data systems, not simply worked within existing ones.
\\n - Strong judgment across data platforms, distributed systems, software architecture, data modeling, cloud infrastructure, and engineering tradeoffs.
\\n - A track record of creating stronger engineering execution: clearer planning, better estimation, dependable delivery, healthier release processes, and improved cross-functional coordination.
\\n - Recent enough hands-on engineering experience to establish credibility with senior engineers and make meaningful architectural and implementation decisions.
\\n - Proven experience recruiting, managing, mentoring, and retaining strong technical talent.
\\n - Familiarity with AI-native engineering workflows and a thoughtful perspective on when AI tools increase leverage-and when they create security, quality, reliability, or governance concerns.
\\n - Comfort working in security-conscious environments and an interest in developing deeper knowledge of public-sector technology, sensitive-data handling, and relevant compliance frameworks.
\\n - Experience operating in an early-stage or rapidly scaling company where priorities evolve quickly, systems are still being built, and leaders must be comfortable with ambiguity.
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