Technical Data Product Manager

Machinify is a leading healthcare intelligence company focused on delivering value and efficiency to health plan clients. The Senior Technical Data Product Manager will drive the data product roadmap, collaborating with technical leadership to translate business needs into product requirements and ensure successful execution across teams.

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

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
• 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
• 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