Machinify · 2 weeks ago
Product Manager | Technical Data
Palo Alto, CA
Full-time
Hybrid
Senior Level, Lead/Staff
10+ years expMachinify is a leading healthcare intelligence company that provides innovative solutions across the payment continuum. They are seeking a Senior Technical Data Product Manager to drive the data product roadmap, collaborating closely with data engineering and architecture teams to translate business needs into actionable product requirements.
AnalyticsArtificial Intelligence (AI)Business IntelligenceMachine LearningPredictive AnalyticsSaaS
Responsibilities
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
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
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
Qualification
Required
10+ years total professional experience
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
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
Agile/Scrum methodology experience
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
Can envision target architectures that will scale 10-100x beyond current state, articulate why they matter, and define pragmatic paths to get there
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
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
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
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
Understand second and third-order effects of architectural decisions across platform, products, and operations
Preferred
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
Company
Machinify
Machinify is a SaaS platform that enables non-technical enterprises to build AI-powered products and processes.
1001-5000 employees
H1B Sponsorship
Machinify has a track record of offering H1B sponsorships. Please note that this does not
guarantee sponsorship for this specific role. Below presents additional info for your
reference. (Data Powered by US Department of Labor)
Distribution of Different Job Fields Receiving Sponsorship
Represents job field similar to this job
Trends of Total Sponsorships
2025 (12)
2024 (6)
2023 (3)
2022 (3)
2021 (4)
2020 (5)
Funding
Current Stage
Late StageTotal Funding
$12.79MKey Investors
Battery Ventures
2025-01-10Acquired
2018-10-08Series A· $10M
2016-03-15Seed· $2.79M
Leadership Team
Prasanna Ganesan
CTO & Board Member
Recent News
2025-12-19
Dallas Morning News
2025-10-24
globallegalchronicle.com
2025-10-24
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