# Technical Data Product Manager

**Company:** [Machinify](https://hotfix.jobs/companies/machinify)
**Location:** Remote
**Role:** Product Management
**Experience:** 5+ years
**Skills:** SQL, ETL, Data Architecture, Data Modeling, Lakehouse, Real-Time Processing, Ai/Llm, Data Engineering, Data Science, Canonical Data Models
**Posted:** 2026-04-09

> Drives data product roadmap and execution in partnership with data engineering and architecture leaders, translating business needs into technical requirements for healthcare payment platforms. Requires hands-on data engineering experience, SQL proficiency, and ability to navigate legacy systems.

## Job Description

## Core 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
- 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

## Required Qualifications

### Experience Requirements
- 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

### Technical Skills (Must-Have)
- **SQL** (writing and reviewing)
- Data architecture review
- ETL pipelines
- Data models
- Distributed architectures

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**Apply:** https://hotfix.jobs/jobs/7b63d6d5-7fb3-47a2-8553-9887764f3473
**Canonical:** https://hotfix.jobs/jobs/7b63d6d5-7fb3-47a2-8553-9887764f3473