Job Description
Meet Fetch AI & Data
AI & Data at Fetch sit at the center of how we understand our business, make decisions, and build intelligent products. The organization operates as an integrated AI & data ecosystem, spanning multiple disciplines, including data engineering, analytics engineering, machine learning, experimentation, and data platforms, all working together to turn data into durable business and customer impact.
Teams operate in complex problem spaces where requirements evolve, tradeoffs are constant, and the right answer is rarely obvious. Success depends on strong technical judgment, comfort with ambiguity, and the ability to gather context and make informed decisions while balancing quality, performance, scalability, and responsible use.
Practitioners across this org contribute hands-on to production systems, analytical foundations, and intelligent features. You will collaborate closely with product, platform, and engineering partners, help shape standards and best practices, and ensure our AI and data capabilities scale reliably as Fetch grows.
About the Role
Fetch is looking for a Director, Solutions Engineering to lead a multidisciplinary domain across Solutions Engineering, Analytics Engineering, and Data Engineering. This leader will own measurable outcomes across client-facing technical delivery, analytics quality, data reliability, product progress, engineering standards, team effectiveness, and business impact.
The Director will define technical and operational direction for a business-critical solution domain, ensuring systems, services, data products, and delivery practices evolve cohesively. This role requires a leader who can translate ambiguous client, product, business, data, and technical needs into clear strategies, scalable solutions, durable operating practices, and measurable outcomes.
The ideal candidate is strong in analytics and data engineering, energized by client-facing work, and experienced in leading through managers, senior individual contributors, and cross-functional stakeholders. They will partner closely with Product, Engineering, Go-to-Market, Client Success, Analytics, and Data teams to align priorities, resolve dependencies, and ensure solutions are client-ready, analytically sound, scalable, reliable, and maintainable.
What You’ll Do
Domain Strategy and Outcomes
- Own domain-level outcomes across technical quality, product progress, solution delivery, analytics quality, data reliability, team effectiveness, and business impact.
- Define technical and operational direction for a client-facing solution domain or problem space.
- Translate ambiguous business, client, product, data, and technical problems into clear strategies, roadmaps, system-level decisions, and execution plans.
- Ensure architectural coherence across related systems, services, integrations, data products, analytics workflows, and client-facing solutions.
- Balance near-term delivery with reliability, scalability, developer experience, data quality, technical debt reduction, and long-term maintainability.
- Align stakeholders on priorities, sequencing, ownership, and trade-offs.
Technical, Analytics, and Data Leadership
- Own the scalability, reliability, data quality, maintainability, and long-term evolution of a business-critical technical domain.
- Guide technical and analytical decisions with long-term implications across multiple systems, teams, or client use cases.
- Raise engineering, analytics, and data standards through design reviews, data quality expectations, system ownership norms, and solution review practices.
- Establish shared patterns, frameworks, quality gates, observability practices, and operating principles that improve leverage across teams.
- Identify and address systemic technical, analytical, data, or platform risks before they impact delivery, client trust, or team productivity.
Execution and Operational Excellence
- Define and drive initiatives that improve system architecture, platform capability, solution scalability, analytics quality, delivery effectiveness, or engineering leverage.
- Convert ambiguous needs into execution plans with clear owners, milestones, decision points, and measurable outcomes.
- Eliminate systemic inefficiencies across teams, including duplicated systems, unclear ownership, inconsistent standards, platform gaps, and recurring delivery friction.
- Drive execution beyond direct reporting lines by aligning dependencies, clarifying decision ownership, and resolving misalignment.
- Use incidents, quality metrics, roadmap progress, client impact, technical health trends, and team health signals to inform prioritization.
- Build operating rhythms that reinforce accountability for outcomes, not activity.
People Leadership and Org Health
- Lead, coach, and develop managers, Solutions Engineers, Analytics Engineers, Data Engineers, senior ICs, and technical leads.
- Build leadership capability in people management, technical judgment, systems thinking, design quality, and ownership.
- Set consistent expectations for technical rigor, analytical accuracy, delivery accountability, operational excellence, and system ownership.
- Hold leaders accountable for delivery outcomes, technical quality, team health, and sustainable execution.
- Ensure fair and consistent performance management, calibration, feedback, and talent development across teams.
- Proactively address retention risks, disengagement, workload imbalance, unclear ownership, equity gaps, and structural friction.
- Build psychologically safe, inclusive environments that support healthy debate, constructive conflict, and long-term technical thinking.
Cross-Functional and Client-Facing Influence
- Partner with Product, Engineering, Analytics, Data, Go-to-Market, Client Success, and peer leaders to align on goals, dependencies, delivery trade-offs, and client outcomes.
- Influence technical and cross-functional direction by aligning stakeholders on architectural decisions, analytics quality, data reliability, scalability, and maintainability.
- Communicate technical direction, data risks, analytical insights, architectural trade-offs, and delivery implications to technical, non-technical, executive, and client-facing audiences.
- Resolve escalations before they damage trust or slow execution.
- Institutionalize standards, operating principles, decision-making mechanisms, and accountability models that improve consistency across the organization.
Minimum Qualifications
- 8+ years of experience in solutions engineering, analytics engineering, data engineering, software engineering, platform engineering, technical implementation, or a related technical discipline.
- 4+ years of technical leadership or people management experience.
- Experience leading managers, technical leads, senior individual contributors, or multiple related technical workstreams.
- Experience owning domain-level outcomes across client-facing technical solutions, analytics deliverables, data products, integrations, platforms, or product engineering areas.
- Proven ability to translate ambiguous business, client, product, data, and technical problems into strategies, roadmaps, execution plans, and measurable outcomes.
- Strong technical judgment across analytics, data modeling, data pipelines, architecture, integration design, scalability, reliability, maintainability, observability, and operational risk.
- Track record of improving technical quality, delivery performance, data reliability, analytical rigor, system ownership, or engineering standards across teams.
- Experience guiding technical or analytical decisions with long-term implications across systems, services, integrations, or data products.
- Demonstrated ability to influence peer leaders, managers, senior ICs, and cross-functional stakeholders without relying solely on authority.
- Strong communication skills with the ability to explain technical, analytical, architectural, and data trade-offs to technical, non-technical, executive, and client-facing audiences.
Preferred Qualifications
- Experience managing managers or scaling leadership through managers, technical leads, and senior ICs.
- Experience in client-facing, analytics, data, platform, integration, ad tech, martech, marketplace, consumer product, or high-scale distributed systems environments.
- Experience establishing domain-level technical standards, architectural principles, design review mechanisms, incident practices, system ownership models, or data quality expectations.
- Experience improving engineering or analytics productivity through planning, tooling, automation, quality gates, observability, shared platforms, or developer experience improvements.
- Experience eliminating systemic inefficiencies across teams, such as duplicated systems, unclear ownership, inconsistent implementation patterns, or platform gaps.
- Experience partnering closely with Product, Go-to-Market, Client Success, Sales Engineering, Analytics, Data, Operations, or executive stakeholders.
- Experience leading through ambiguity, organizational change, scaling complexity, or high-pressure client delivery environments.
- Experience building scalable systems for hiring, planning, performance management, calibration, leadership development, or operating rhythms.
- Practical fluency with AI-assisted engineering, analytics, or productivity tools, including judgment around use cases, risks, validation, data accuracy, governance, and team adoption.
Compensation: At Fetch, we offer competitive compensation packages including base, equity, and benefits to the exceptional folks we hire. Discover our benefits and how our employees live rewarded at
https://fetch.com/careers
