• Develop in an agile environment, including TDD and flexible pair programming • Contribute to balanced teams, working with product and design to shape the application • Consistently deliver quality code that supports business goals • Understanding of data structures, TDD, and software engineering design patterns and principles • Coach up to 5 engineers in career growth and improvement • Guide the engineering practice across teams to create more effective methods • Hybrid IC/manager position with up to 50% hands-on coding • Participate in an on-call rotation, ensuring timely resolution of critical system issues, and contributing to the continuous improvement of our system reliability and availability • Collaborate with cross-functional teams to design and deploy AI features that improve user experience and operational efficiency

Role Description Join GoDaddy's Forge Ops team at the intersection of Data, Infrastructure, and AI-driven operations. As Senior Manager, Systems Engineering, you will lead the reliability, cost efficiency, and agentic operation of the Data & AI ecosystem that serves GoDaddy. This is a deeply technical leadership role, not a hands-off manager position. You will operate as GoDaddy's L1/L2 authority over critical analytics and data platforms while advancing Forge Operations: a structured operating model designed to transition platform operations from hero-based, expert-dependent support to system-based, agent-assisted, self-improving operations. If you can translate a business problem into a technical architecture and that architecture into team execution — and you want to build the AI Ops pattern for a large-scale data organization, this role is for you. What you'll get to do... - Own and operate GoDaddy's analytical and data intelligence platforms (Redshift, QuickSight, FeedDB, Protegrity, Alation) as the authoritative L1/L2 platform owner — driving reliability, deployment standards, cost optimization, and user enablement across an ecosystem with a 50PB+ data lake and thousands of consumers. - Lead 24/7 incident management and production operations across 10+ Data & AI platforms, owning MTTR/MTTD targets, AAR rigor, and a root-cause-to-control loop that converts every incident into a runbook, monitoring improvement, or automation — not just a resolved ticket. - Architect and advance Forge Ops OS, the team's agent-based operating model, using history-informed early warning, auto-recovery agents, runbook intelligence, and bounded agentic orchestration. - Drive data platform cost efficiency through unit economics — cost per query, cost per workload, cost per dashboard visit — translating AWS spend into measurable business metrics and continuous optimization across Redshift, QuickSight, DPaaS, and ML infrastructure. - Manage operational planning and executive reporting weekly, monthly, and quarterly. Run a sprint-based improvement program with a near 70% strategic allocation. Provide clear traceability from team execution to company goals and landmark outcomes. Qualifications - 5+ years validated 24/7 production operations leadership — leading incident response end-to-end, owning MTTR performance, leading post-mortems (AARs) that produce controls, and driving the systemic fixes that reduce incident recurrence. - Hands-on AWS architecture/platform expertise — Redshift, EMR/Airflow, Lambda, EKS, S3, IAM/RBAC, and CDK/CloudFormation — with end-to-end operational and cost ownership of at least two production data or analytics platforms. - Systems and software architecture fluency — able to translate business requirements into scalable technical designs, reason about architectural trade-offs, and decompose solutions into actionable engineering tasks without deferring all technical judgment to individual contributors. - Data platform operations at scale — ETL/ELT pipelines, data lakes, orchestration frameworks (Airflow, EMR), and BI tooling — with deep understanding of data quality, SLAs, lineage, and the dependency chains that connect producers to executive-facing consumers. - Technical team leadership with operational rigor — proven ability to lead engineers through sprint-based planning, capacity management, and cross-functional delivery, while maintaining the hands-on technical credibility to unblock, review, and elevate the team's output. Requirements - Experience with AI/agentic operations — building or operating LLM-based tools such as automated runbooks, incident response agents, AAR generation systems, or bounded auto-recovery workflows. - Familiarity with graph databases or lineage/observability architectures (e.g., Neptune or equivalent) for dependency mapping, early warning, and blast-radius analysis in large data ecosystems. - Hands-on experience with Databricks or analytical compute platforms (Lakehouse, feature stores, ML infrastructure) in a production operations context. - Experience with data protection platforms (e.g., Protegrity) and PII/tokenization workflows in large-scale data lake or analytics environments. - Familiarity with ServiceNow/CMDB or equivalent incident management systems (Jira, PagerDuty) as operational systems of record — including MTTR/MTTD tracking and CI/lineage integration. Benefits - Paid time off - Retirement savings (e.g., 401k, pension schemes) - Bonus/incentive eligibility - Equity grants - Participation in our employee stock purchase plan - Competitive health benefits - Other family-friendly benefits, including parental leave

• Collaborate with Product: Translate product goals into actionable, outcome-focused technical plans • Lead technical direction across multiple initiatives: design, trade-offs, platform development, and system integrity • Own architecture: define clear technical patterns and frameworks that balance speed with maintainability • Be an escalation point for engineers: support teams in resolving complex technical issues • Elevate engineering quality: strengthen testing, CI/CD, release practices, observability, and performance • Drive operational excellence: lead incident response, security improvements, and reliability reviews • Advance AI transformation in engineering: support the team in effectively using AI tools across the SDLC • Foster technical growth of teams: mentor technical leads and senior engineers

• Design, execute, and measure AI-Native software development and quality engineering experiments. • Identify engineering bottlenecks where AI-Native workflows can improve productivity, quality, speed, developer experience, or release confidence. • Evaluate emerging AI engineering tools, coding agents, AI-enabled development environments, test generation tools, code review assistants, documentation tools, and developer productivity platforms. • Develop and institutionalize AI-Native development, testing, review, documentation, refactoring, debugging, and delivery practices. • Define and maintain engineering quality bars, operating standards, usage guardrails, workflow templates, and best practices for AI-assisted software development. • Create AI-Native quality engineering practices that improve test automation, regression prevention, validation, code review, quality gates, and production readiness. • Establish balanced metrics and measurement frameworks for engineering productivity, quality, cycle time, developer experience, adoption, and business impact. • Analyze experiment results and recommend whether practices should be adopted, modified, scaled, or retired. • Create playbooks, frameworks, operating models, and enablement materials that turn successful experiments into repeatable practices across the organization. • Coach engineers and engineering leaders to maximize effectiveness through AI-assisted development, agentic workflows, quality engineering, and human-AI collaboration. • Drive organization-wide adoption of proven AI-Native engineering practices through coaching, enablement, influence, measurement, and continuous feedback loops. • Define safe and responsible practices for AI-generated code, AI-assisted testing, tool usage, data exposure, IP protection, security, maintainability, and human review. • Partner with engineering, product, QA, security, DevOps, platform, and executive leadership to align AI-Native transformation efforts with business priorities. • Continuously improve software development, QA, automation, CI/CD, DevOps, cloud engineering, observability, security, and delivery processes through AI-Native approaches. • Develop strategic recommendations for the future evolution of software engineering at Sparkrock.