Kaseya

• Design and implement the enterprise internal audit and internal controls framework, including governance structures, policies, and quality standards • Develop and manage a multi-year internal audit and controls roadmap, driving continuous improvement initiatives and program scalability • Establish control evaluation criteria, risk assessment methodologies, and audit coverage strategies • Build and oversee business-performed controls testing programs, including standardized scripts, documentation, and evidence requirements • Lead risk-based internal audits across financial, operational, and compliance areas, including planning, execution, reporting, and follow-up • Identify control gaps and improvement opportunities, and communicate findings and recommendations to senior leadership • Partner with business leaders and control owners to develop, track, and validate remediation plans for identified deficiencies • Leverage data analytics and automation to improve testing efficiency, coverage, and overall program effectiveness

• Own and lead the design of scalable, resilient, and maintainable system architectures • Define architectural standards, engineering best practices, and technology direction • Ensure alignment between system architecture and long-term business objectives • Drive architectural reviews and make high-impact technical decisions • Actively contribute to the codebase in Java (Spring Boot, microservices) • Design and implement high-performance, fault-tolerant distributed systems • Lead by example through clean code, code reviews, and engineering discipline • Troubleshoot and resolve complex production and scalability challenges • Architect and build cloud-native solutions on AWS (ECS, EC2, SQS, DynamoDB, RDS) • Design scalable database solutions (MySQL/PostgreSQL) with a focus on performance and optimization • Guide containerization and orchestration strategies (Docker, Kubernetes) • Provide technical leadership and mentorship to engineers across the team • Support career growth and elevate engineering standards • Lead technical planning, estimation, and execution • Foster a culture of accountability, collaboration, and continuous improvement • Partner with Product and stakeholders to shape technical solutions • Ensure reliability, observability, and performance through monitoring and alerting solutions (e.g., Prometheus, Grafana, CloudWatch) • Promote DevOps practices and maintain robust CI/CD pipelines (GitHub Actions, GitLab CI, Jenkins) • Drive engineering quality, scalability, and operational readiness.

• Analyze, document, and assess end‑to‑end supply chain and manufacturing processes, identifying inefficiencies, risks, and opportunities for optimization. • Lead initiatives to improve supply chain operations through technology adoption, ERP optimization, and process redesign, ensuring solutions are scalable, repeatable, and aligned with best practices. • Partner with business and IT teams to design and improve processes within ERP systems (Oracle preferred), ensuring configurations support operational needs and industry standards in a manufacturing environment. • Develop, standardize, and maintain policies, Standard Operating Procedures (SOPs), and work instructions to drive consistency, compliance, and operational clarity. • Identify and implement continuous improvement opportunities that enhance efficiency, quality, and cycle times across supply chain and manufacturing operations. • Support cross‑functional initiatives by providing supply chain process expertise, ensuring system and process changes align with business objectives. • Identify, document, and embed process controls to mitigate risk and support compliance with internal and external requirements. • Lead process change communication and training efforts to drive adoption and ensure stakeholders understand new ways of working.

• Explore and analyze data using Python, pandas, and PySpark (or similar tools). • Use matrix factorization, clustering, dimensionality reduction, and related techniques to understand and prepare data for modeling, and to identify and label latent factors (e.g., user behavior patterns, content/topic clusters, expertise dimensions). • Create, tune, and productionize ML models for: • Categorization / classification • Recommendations and similarity • Other prediction or ranking tasks that power product features • Design and implement AI-driven ingest flows that turn unstructured inputs (tickets, emails, forms, messages, logs, etc.) into well-structured data that models and downstream systems can use. • Build workflows where AI can: • Auto-fill or suggest key fields and metadata. • Proactively ask users/customers for missing or ambiguous information (e.g., via email or messaging). • Surface similar past items or solutions to assist humans in decision-making. • Fully handle simple, repetitive “Level 1” style requests end-to-end when safe to do so. • Work closely with engineers to integrate models and workflows into production systems with proper monitoring, fallbacks, and guardrails. • Work with multiple product teams to help them identify and scope AI opportunities in their areas. • Define patterns, templates, and best practices for data ingestion, feature creation, model usage, and evaluation that teams can reuse. • Serve as a trusted advisor and technical lead: • Provide design and architecture guidance on data and ML-heavy features. • Join projects to handle the most complex modeling or workflow automation pieces when teams get stuck. • Mentor and guide junior data/ML engineers and analysts: • Conduct code and model reviews. • Pair with them on tricky problems. • Help them develop good intuitions about metrics, evaluation, and operational reliability. • Help establish and socialize standards for experimentation, documentation, and responsible AI usage across teams.

• Own the architecture and technical direction for key product areas in a multi-tenant SaaS platform. • Work with product, design, and data/ML teams to translate business problems into simple, robust technical solutions. • Drive the evolution of our system architecture (APIs, services, data flows, auth, tenancy, integrations) as the product and customer base scale. • Build and maintain backend services and APIs (REST/GraphQL/gRPC) that power those experiences. • Deliver backend features end-to-end including data schemas and business logic. • Collaborate with UX and product to ensure responsive and delightful product experiences. • Partner with data scientists and MLOps / platform teams to embed data, ML and AI capabilities into the product (recommendations, categorization, automation, routing, insights, LLM-powered workflows, etc.). • Design APIs, data contracts, and UX flows that make ML/AI features reliable, understandable, and safe for customers. • Ensure telemetry and feedback loops are in place so data/ML teams can measure performance, iterate models, and improve outcomes. • Help define and implement guardrails for AI features (fallbacks, explanations, error handling, permissions). • Champion operability: monitoring, alerting, logging, and runbooks for services you own. • Lead efforts to improve performance, scalability, and resilience of critical paths (e.g., onboarding, reporting, AI-assisted workflows). • Work with security and compliance to ensure features meet requirements around authentication, authorization, data privacy, and multi-tenancy. • Participate in and help evolve on-call / incident response processes as a technical leader. • Act as a technical mentor for multiple teams, raising the bar on code quality, reviews, testing, and design. • Lead technical design reviews and cross-team architecture discussions, especially where product, data, and ML intersect. • Help define engineering standards and best practices (API design, frontend patterns, error handling, observability, testing). • Partner with engineering management to shape roadmaps, staffing, and sequencing for major initiatives.

• Lead end‑to‑end strategic and analytical workstreams, including market sizing, competitive benchmarking, pricing strategy, business case development, and performance analysis • Build and maintain financial models to assess strategic initiatives, investment opportunities, and go‑to‑market decisions • Develop bespoke, on‑the‑fly analyses to bring quantitative rigor to a wide range of complex business problems • Synthesize data and analysis into clear, compelling, executive-ready narratives, presentations, and recommendations • Partner with senior leadership to support strategic planning, prioritization, and decision-making processes • Facilitate cross‑functional working sessions, manage project timelines, and align stakeholders across fast‑moving initiatives • Support preparation of materials for executive and board-level strategic reviews • Champion the use of AI and advanced analytics tools to improve the quality, speed, and scale of strategy work • Monitor industry trends, competitive dynamics, and emerging technologies to inform strategic insights and recommendations • Contribute to a high‑performance strategy team culture through structured thinking, ownership, and collaboration

• Drive strategic and operational initiatives impacting our customer base and customer-facing teams • Develop and lead impactful, measurable strategies that drive revenue growth and meaningful interactions with customers • Lead workstreams with cross-functional teams as necessary to ensure we are bringing the best thinking to key initiatives • Manage special projects across functions and departments

• Drive strategic and operational initiatives impacting our customer base and customer-facing teams • Develop and lead impactful, measurable strategies that drive revenue growth and meaningful interactions with customers • Lead workstreams with cross-functional teams as necessary to ensure we are bringing the best thinking to key initiatives • Manage special projects across functions and departments • Lead and develop a cross-functional team with diverse strategy and analytics backgrounds as they execute on key projects and hunt for insights to drive company strategy

• Drive strategic and operational initiatives impacting our customer base and customer-facing teams • Develop and lead impactful, measurable strategies that drive revenue growth and meaningful interactions with customers • Lead workstreams with cross-functional teams as necessary to ensure we are bringing the best thinking to key initiatives • Manage special projects across functions and departments

• Lead initiatives to build intelligent insights and automated analysis workflows that improve efficiency and decision-making. • Identify high-impact Finance Operations use cases and translate them into scalable analytical and decision-support solutions. • Partner with Finance Operations, Data, and other cross-functional teams to design, scale, and automate financial data products and dashboards. • Lead the design of scalable datasets, analytical models, and dashboards that enable forecasting, performance tracking, and risk management. • Design robust datasets and perform complex SQL queries across multiple systems to ensure data accuracy, usability, and scalability. • Lead complex analyses (e.g., revenue, cash flow, bad debt) to uncover drivers of performance and turn insights into measurable outcomes. • Present insights to senior leadership and produce executive-level materials with clear, data-driven narratives. • Influence cross-functional teams (Finance, Finance Operations, Sales, Data Engineering) by bringing structure to ambiguity and driving alignment.