Principal Technical Product Manager

We are seeking a highly technical, customer-obsessed Product Manager to lead the strategy, roadmap, and evolution of our Enterprise Data & AI Platform. The platform enables engineering teams, data scientists, analysts, and developers to build and operate data products, analytics, machine learning solutions, and AI applications at enterprise scale.

This role owns the three-year platform vision and translates customer needs into technical capabilities, investment priorities, and executable roadmaps. The successful candidate will balance customer value, operational reliability, cost efficiency, and delivery speed.

We are looking for someone equally comfortable discussing strategy with executives and working directly with engineers to evaluate architectures, investigate technical challenges, and develop practical solutions.

Key Responsibilities:

Product Strategy and Roadmap

• Own the three-year vision, strategy, and roadmap across data engineering, analytics, compute, governance, developer experience, and AI infrastructure.
• Translate customer needs into annual goals, quarterly milestones, investment priorities, and measurable business outcomes.
• Evaluate build-versus-buy decisions, emerging technologies, platform consolidation, and architectural trade-offs.
• Develop investment proposals balancing customer value, scalability, operational complexity, execution risk, and total cost of ownership.

Customer Discovery and Product Development

• Build a deep understanding of customers, including data engineers, developers, data scientists, ML engineers, and business teams.
• Conduct customer interviews, workflow observations, technical discovery, and usage analysis to identify unmet needs.
• Translate insights into product requirements, working-backwards narratives, business cases, and prioritized roadmaps.
• Establish feedback mechanisms and measure adoption, customer satisfaction, developer productivity, and delivered value.

Technical Product Leadership

• Work directly with engineers and architects to evaluate technical designs, investigate problems, and make informed implementation trade-offs.
• Apply strong knowledge of Databricks, AWS, Apache Spark, lakehouse architectures, streaming, orchestration, and data governance.
• Understand AI/ML infrastructure, model serving, LLMs, AI gateways, agentic applications, and production AI operations.
• Evaluate performance, scalability, security, reliability, interoperability, and cost when making technology decisions.

Execution and Operational Excellence

• Translate product strategy into executable plans with clear priorities, milestones, dependencies, and accountability.
• Drive capabilities that improve reliability, automation, self-service, and developer productivity.
• Balance feature delivery with technical debt, operational stability, and platform maintenance.
• Establish success metrics for availability, incident recovery, onboarding time, adoption, and delivery efficiency.

Financial and Cross-Functional Leadership

• Develop business cases and TCO models covering cloud consumption, licensing, infrastructure, engineering, and operations.
• Partner with engineering and finance teams on consumption forecasting, cost attribution, optimization, and investment planning.
• Evaluate vendor capabilities, pricing, performance benchmarks, and commercial trade-offs.
• Influence engineering, security, architecture, finance, and business leaders to align priorities, resolve dependencies, and drive decisions.

Basic Qualifications:

• Bachelor’s degree in Computer Science, Computer Engineering, Electrical Engineering, or a related field
• A minimum of 8 years of experience in technical product management, software engineering, data engineering, or platform engineering, including ownership of complex technical products.
• Demonstrated experience developing multi-year product strategies, roadmaps, investment plans, and measurable outcomes.
• Strong technical knowledge and practical experience with Databricks, AWS, distributed data processing, lakehouse architectures, streaming, and data governance.
• Experience working directly with engineers and architects on architecture, performance, reliability, cost, and implementation trade-offs.
• Proven customer research, analytical, communication, and cross-functional leadership skills, with the ability to navigate ambiguity and drive execution.

Preferred Qualifications:

• Previous hands-on software engineering, data engineering, or cloud platform engineering experience.
• Experience building enterprise data platforms serving multiple engineering and business organizations.
• Experience with Spark, Delta Lake, Unity Catalog, MLflow, Kubernetes, Terraform, Airflow, or similar technologies.
• Familiarity with AI/ML platforms, LLM serving, AI gateways, agentic applications, and cloud FinOps.
• Experience with large-scale streaming, telemetry, IoT, or distributed systems. Automotive or manufacturing experience is a plus, not required.

What Success Looks Like

Within 90 days, you will understand the platform's customers, architecture, economics, and operational challenges. Within six months, you will establish a customer-backed three-year strategy, prioritized roadmap, investment plan, and measurable goals. Within the first year, you will demonstrate improvements in developer productivity, customer experience, reliability, adoption, or cost efficiency. Success means translating strategy into execution and delivering measurable business value.

What Makes Someone Successful

You are customer-obsessed, technically credible, and comfortable working directly with engineers on complex problems. You combine strategic thinking with execution discipline, turning customer insights into practical plans and measurable results. You understand the trade-offs between innovation, reliability, cost, and operational complexity. You influence decisions across organizations, challenge assumptions with data, and take ownership of outcomes.

You think like a product owner, understand technology like an engineer, and execute with accountability for customer and business value.

Clarify the role’s level and decision authority. Add concrete platform customer outcomes