Senior Product Manager, Data Platform

Curinos Inc is hiring a Senior Product Manager to lead the Data Platform pod and own the end-to-end vision, roadmap, and outcomes for Curinos’ Databricks Lakehouse. This role is responsible for expanding the Customer 360 data layer, enabling AI-ready governed data at scale, and ensuring platform operations support delivery across pods.

Role scope

As the platform product owner, you will manage the Data Platform across the full Databricks Lakehouse lifecycle, from ingestion and modeling through governance, data products, and shared enterprise data assets. You will also drive how trusted, well-modeled data products are exposed for internal customer use cases, including Customer 360 gold assets built for business-domain decisioning.

Responsibilities

• Own the Data Platform vision, roadmap, and quarterly outcomes across the Databricks Lakehouse, including governance, ingestion, modeling, data products, and shared enterprise data assets, with explicit ownership of the Customer 360 data layer.
• Bring data from multiple pods together into trusted, well-modeled data products across business domains, anchored by Customer 360 gold assets and supported by a central catalog used by pods and decisioning use cases.
• Deliver AI-ready data by default using data contracts, complete metadata, in-pipeline data quality checks, and governed semantic layers.
• Partner with the AI Platform, App & API Platform, product pods, Data Strategy, Engineering, and Advisory to define reusable patterns that pods can adopt and extend.
• Lead and develop one direct report focused on methodology and semantic governance.
• Partner with the Head of Data Strategy on POCs for new data assets, performing hands-on discovery when it accelerates progress.
• Ensure Databricks operations are managed at scale, including compute, permissions, cost allocation, and compliance, in coordination with the teams that run the platform day to day.
• Partner with Databricks to align the enterprise data platform strategy and maturity roadmap with industry direction and Databricks platform evolution.

Requirements

• 6+ years of product management experience, including 3+ years owning data platform, data infrastructure, or analytics platform products.
• Deep, hands-on understanding of Databricks: Lakehouse architecture, Unity Catalog, Delta Lake, Databricks SQL, Lake flow pipelines and jobs, plus compute and cost management.
• Strong knowledge of how a data platform supports a Databricks-native AI platform, including semantic layers and metric views, Genie, vector search, model serving, agents, and MCP-based data access.
• Experience with data governance at scale, including catalogs, metadata, lineage, access controls, data contracts, and data quality frameworks.
• Proven ability to operate a platform as a product for internal customers by driving adoption of shared patterns while balancing central standards with pod autonomy and prioritization trade-offs.
• Experience with customer-level or entity-level data, including identity resolution, data linkage, and handling sensitive data in a regulated environment.
• Technical fluency to partner with data engineers, architects, and data scientists, including comfort with SQL, data modeling, and Lakehouse design patterns such as medallion architecture.
• Demonstrated ability as a strong individual contributor, comfortable being hands-on with data via SQL, Databricks notebook exploration, data profiling, quality and linkage checks, and rapid prototyping.
• Ability to think beyond a single pod to the broader product suite, AI platform, and future value delivered by data.
• Strong communication skills for explaining complex data and AI concepts to executives and technical and non-technical stakeholders.
• Experience managing, developing, and/or mentoring product managers.
• Strong judgment and ownership, with capability to manage multiple workstreams, own quarterly outcomes, and make practical trade-offs.

Strategic vision

• Own the product vision and roadmap for the full Data Platform, covering the Databricks Lakehouse end to end, the Customer 360 data layer, and shared data tooling.
• Prioritize investments across strategic pillars: powering AI-ready decisioning, enabling customer-level decisioning, centralizing institutional knowledge, and ensuring Databricks operations scale reliably.
• Adopt native Databricks capabilities when they offer long-term value to reduce custom engineering over time.
• Shape the roadmap with a broader view that includes Curinos product suite needs and the AI Platform.

Customer 360 data layer

• Grow the breadth and depth of the Customer 360 data layer by consolidating Curinos datasets into a unified customer-level view aligned with client agreements and data-use permissions.
• Define and run a repeatable intake and operating model for Customer 360, including when new assets are added, how they are prioritized, and how they are governed.
• Partner with Data Strategy and business, legal, compliance, and risk leaders to ensure identity, linkage, privacy, and data-use controls protect client confidentiality and trust.

AI-ready data foundation

• Work closely with the AI Platform team to make Curinos data ready to power models, agents, and AI-assisted experiences under the same governance and access controls used across the Lakehouse.
• With your direct report, ensure governed semantic layers keep business meaning consistent in the platform rather than in prompts, improving accuracy and reducing AI interaction cost.
• Expose governed data products, semantic assets, and metadata through standardized interfaces that models, agents, and MCP-enabled experiences can safely consume.

Governance, contracts and data quality

• Make machine-readable data contracts based on the Open Data Contract Standard, including complete metadata and in-pipeline data quality checks as standard steps for production promotion.
• Increase the number of documented and certified assets in Unity Catalog, ensuring clear ownership, certification, lineage, and access controls across Lakehouse layers.

Databricks platform operations

• Enable reliable Databricks operations as usage grows across pods, partnering with day-to-day platform teams on compute management, project and workspace setup, permissions and access requests, catalog structure, cost allocation, and compliance controls.
• Stay current on Databricks roadmap items, reserve capacity for proactive exploration, and translate learnings into a unified Data and AI roadmap.

Technologies

• Databricks; Databricks Lakehouse; Unity Catalog; Delta Lake; Databricks SQL; Lake flow pipelines; Genie; vector search; model serving; agents; MCP; SQL; medallion architecture; Open Data Contract Standard; Databricks notebooks; Delta Sharing; Snowflake

Location and compensation

Location: New York, NY (hybrid).
Salary: USD 160,000 to 170,000 per year (plus bonus).
Minimum experience: 6 years.