Product Manager

MachinifyRemote - Palo Alto, California, USA3w ago

Remote Senior NA Payments Artificial Intelligence Cloud Computing Product Manager Analytics Engineer Data Engineer Excel AWS Team Management SQL Azure

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Requirements

5+ years in product management roles
Prior hands-on experience as data engineer, data scientist, or analytics engineer (required)
Proven track record shipping data products or platforms used by internal/external teams
Experience driving execution in matrixed organizations without direct authority
Demonstrated ability to assess complex technical landscapes and define future-state architectures
Data architecture expertise: Deep understanding of data modeling, normalization/denormalization, distributed systems, batch/streaming patterns, ETL/ELT design
Advanced SQL proficiency: Write complex queries, optimize performance, understand CDC patterns, validate data quality
Cloud data infrastructure: AWS preferred (S3, Spark, RDS, DMS, Glue) or equivalent GCP/Azure experience
Modern data stack fluency: Knowledge of data warehouses, lakehouse formats, orchestration tools (Airflow), transformation frameworks (DBT), BI platforms
Analytical rigor: Define metrics, analyze data, make data-driven decisions, identify patterns across complex systems
Product Management Excellence (Must-Have)
Strong stakeholder management across technical teams (data engineering, data science, platform) and business audiences
Ability to translate complex technical architectures into business outcomes and vice versa
Experience defining product vision, building roadmaps, and measuring success
Proven influence without direct authority—building consensus through credibility and data-driven arguments
Excellent written and verbal communication across all organizational levels
Critical Success Factors
Current State Mastery: Exceptional ability to rapidly understand complex legacy systems—navigating five different platforms with different data models, ETL patterns, and team cultures to discover how things actually work
Future State Vision: Can envision target architectures that will scale 10-100x beyond current state, articulate why they matter, and define pragmatic paths to get there
Execution Velocity: Move with extraordinary speed from analysis to decision to implementation. Bias toward shipping 80% solutions today over 95% solutions next quarter. Understand that speed is a competitive advantage.
AI-Augmented Productivity: Active, sophisticated use of AI tools (ChatGPT, Claude, Copilot, etc.) to accelerate analysis, generate SQL, synthesize information, draft documentation, and make faster decisions than traditional approaches
Technical Credibility: Data engineering and data science teams respect you because you can engage substantively in architectural discussions, understand their constraints, and spot issues before they become problems
Cross-Boundary Navigation: Excel at finding critical information across disparate systems and tribal knowledge; build trust across teams with different cultures and priorities; serve as connective tissue in high-pressure environments
Systems Thinking: Understand second and third-order effects of architectural decisions across platform, products, and operations
Experience with LLM/AI applications for data transformation, code generation, or workflow automation
Python proficiency for data analysis, prototyping, or understanding engineering implementations
Prior data platform migrations or consolidations at significant scale
Healthcare payment integrity, payer operations, or regulated industry experience
Hands-on experience with Snowflake, Databricks, Kafka, Fivetran, or similar modern data platforms
Background in distributed systems, database internals, or data-intensive applications
Fast-paced startup or high-growth company experience
Why This Role Matters
Transformational Impact: Your work enables product teams serving 160M+ lives to ship faster and unlocks significant business value across coordination of benefits, subrogation, audit, and payment integrity
Technical Depth: Engage substantively in cutting-edge architecture decisions—lakehouse formats, distributed systems, real-time processing, LLM-powered automation—not just coordinate meetings
Unique Challenge: Navigate five legacy platforms with different data models, technologies, and cultures—partnering with technical leadership to shape unified future state in a once-in-career data consolidation opportunity
Execution Autonomy: We value speed over process. Fast decision-making and shipping results matter more than perfect planning
AI-First Environment: We use Claude, LLMs, and AI-powered tools extensively throughout the organization. You're expected to leverage AI to move faster than traditional approaches
Strategic Partnership: Work alongside VP Data Engineering, CTO, and architecture leadership to shape data platform serving dozens of product teams and supporting a $5B healthcare intelligence platform
Team and Culture
Reporting Structure: Sr. Director of Product Management, Platform & Data
Key Partnerships:
VP of Data Engineering and CTO (primary technical partners for strategy and architecture)
Data Engineering leadership (Eric, Andre, Sam)
Data Science teams
Platform Engineering teams
Product teams across COB, Subrogation, Audit, Pharmacy PI, Complex Claims
Architecture and Technical Strategy leadership
You will work in close partnership with the VP of Data Engineering and CTO, bringing the product lens to technical strategy discussions while they own engineering execution and technical architecture. Your role is to translate business needs into product requirements, coordinate across teams, and ensure data initiatives deliver value to product organizations.
Fast-paced with bias toward action over analysis paralysis
Technically deep—we respect engineering expertise and engage substantively
Lightweight processes that accelerate rather than burden
Remote-first with occasional travel for strategic planning (~quarterly)
Sophisticated use of AI tools to augment productivity expected and encouraged
Technical Environment:
Current: Postgres (Citus), Apache Spark, AWS (S3, DMS, RDS), Python, SQL
Evolving: Lakehouse formats (Delta Lake/Iceberg), streaming capabilities, unified data architecture, LLM-powered automation
Scale: Hundreds of millions of records, petabyte-range data, multi-tenant architecture, dozens of data sources
Equal Employment Opportunity at Machinify
Machinify is committed to hiring talented and qualified individuals with diverse backgrounds for all of its positions. Machinify believes that the gathering and celebration of unique backgrounds, qualities, and cultures enriches the workplace.
See our Candidate Privacy Notice at: https://www.machinify.com/candidate-privacy-notice/

Responsibilities

Product Strategy and Planning
Assess current state rapidly: Work with data engineering and architecture teams to understand complex legacy landscapes—what exists, where critical information lives, and how systems actually work
Contribute to future state vision: Partner with VP Data Engineering, CTO, and architecture leads to shape target data architectures, canonical models, and platform capabilities that will scale to support product teams
Develop product roadmap: Translate business priorities into data product requirements, working with technical leadership to sequence initiatives and balance migration work, new capabilities, and product enablement
Support technical evaluations: Contribute product perspective to build vs. buy decisions, technology evaluations (lakehouse formats, real-time processing, AI-powered automation), and architectural choices
Define and track success metrics: Establish product-level OKRs, track adoption across product teams, and communicate progress to stakeholders
Execution and Coordination
Drive cross-functional delivery: Coordinate data initiatives from requirements through production, working across data engineering, data science, platform engineering, and product teams
Unblock relentlessly: Identify and resolve dependencies, bottlenecks, and blockers before they slow down team velocity
Navigate complexity: Find critical information scattered across legacy platforms, undocumented systems, and tribal knowledge; synthesize insights and create clarity
Facilitate decisions: Build consensus across teams with competing priorities and different technical opinions
Leverage AI extensively: Use LLMs and AI-powered tools to accelerate analysis, documentation, SQL generation, information synthesis, and decision-making
Establish lightweight visibility: Create metrics, dashboards, and reporting that provide insight without creating overhead
Technical Collaboration and Product Enablement
Partner with technical leadership: Work closely with data engineering, data science, and architecture leads—contributing product perspective while respecting their technical expertise and domain ownership
Translate requirements: Convert product team needs into clear technical requirements that engineering teams can execute against
Enable product teams: Ensure downstream product teams can successfully consume data platform capabilities through clear interfaces, documentation, and support
Participate in technical discussions: Engage substantively in reviews of ETL pipelines, data models, distributed architectures, and platform decisions
Bridge stakeholders: Translate complex technical concepts into business value for executives and product teams; bring business context to technical discussions
What You'll Work On
You will partner with data engineering, data science, and platform teams on initiatives such as:
Data consolidation and unification across legacy platforms—working with engineering teams to coordinate migrations to unified infrastructure while maintaining production stability and enabling parallel product development
Canonical data model development—collaborating with data engineering and data science leadership to define product requirements for production-ready models covering medical claims, pharmacy claims, eligibility, and other core healthcare entities
Platform modernization—contributing product perspective to technical evaluations and roadmaps for lakehouse adoption, OLAP/OLTP separation, real-time processing capabilities, and distributed architecture patterns
AI-powered automation—partnering with technical teams to evaluate and implement LLM-based approaches that accelerate ETL development, data transformation, and migration workflows
Data discovery and cataloging—working with engineering to define requirements for capabilities that help teams understand what data exists, where it lives, how to access it, and what it means
Product team enablement—ensuring downstream product teams can successfully consume data platform capabilities through clear interfaces, comprehensive documentation, and responsive support
These represent current focus areas but the role will evolve based on business priorities, strategic direction, and technical roadmap.

Benefits

Transformational Impact: Your work enables product teams serving 160M+ lives to ship faster and unlocks significant business value across coordination of benefits, subrogation, audit, and payment integrity
Technical Depth: Engage substantively in cutting-edge architecture decisions—lakehouse formats, distributed systems, real-time processing, LLM-powered automation—not just coordinate meetings
Unique Challenge: Navigate five legacy platforms with different data models, technologies, and cultures—partnering with technical leadership to shape unified future state in a once-in-career data consolidation opportunity
Execution Autonomy: We value speed over process. Fast decision-making and shipping results matter more than perfect planning
AI-First Environment: We use Claude, LLMs, and AI-powered tools extensively throughout the organization. You're expected to leverage AI to move faster than traditional approaches
Strategic Partnership: Work alongside VP Data Engineering, CTO, and architecture leadership to shape data platform serving dozens of product teams and supporting a $5B healthcare intelligence platform
Team and Culture
Reporting Structure: Sr. Director of Product Management, Platform & Data
Key Partnerships:
VP of Data Engineering and CTO (primary technical partners for strategy and architecture)
Data Engineering leadership
Data Science teams
Platform Engineering teams
Product teams across COB, Subrogation, Audit, Pharmacy PI, Complex Claims
Architecture and Technical Strategy leadership
You will work in close partnership with the VP of Data Engineering and CTO, bringing the product lens to technical strategy discussions while they own engineering execution and technical architecture. Your role is to translate business needs into product requirements, coordinate across teams, and ensure data initiatives deliver value to product organizations.
Fast-paced with bias toward action over analysis paralysis
Technically deep—we respect engineering expertise and engage substantively
Lightweight processes that accelerate rather than burden
Remote-first with occasional travel for strategic planning (~quarterly)
Sophisticated use of AI tools to augment productivity expected and encouraged
Technical Environment:
Current: Postgres (Citus), Apache Spark, AWS (S3, DMS, RDS), Python, SQL
Evolving: Lakehouse formats (Delta Lake/Iceberg), streaming capabilities, unified data architecture, LLM-powered automation
Scale: Hundreds of millions of records, petabyte-range data, multi-tenant architecture, dozens of data sources
Equal Employment Opportunity at Machinify
Machinify is committed to hiring talented and qualified individuals with diverse backgrounds for all of its positions. Machinify believes that the gathering and celebration of unique backgrounds, qualities, and cultures enriches the workplace.
See our Candidate Privacy Notice at: https://www.machinify.com/candidate-privacy-notice/