{"id":3952790,"url":"https://alion.io/job/curinos-senior-product-manager-data-platform","title":"Senior Product Manager, Data Platform","company":{"id":1985814,"name":"Curinos","domain":"curinos.com","url":"https://alion.io/company/curinos","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"ADP","truth_index":{"grade":"A","score":92,"open_postings":3,"ghost_share":0,"stale_share":0.333,"repost_share":0,"time_to_fill_p50_days":47,"computed_at":"2026-11-10T06:52:35Z"}},"role":"Product","role_family":"Product","seniority":"lead","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"explicit","locations":["New York, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":160000,"max":170000,"currency":"USD","period":"year","gross":null,"usd_annual":170000},"salary_estimate":null,"experience_years_min":6,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Copilot","optional":false},{"name":"Databricks","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"Delta Lake","optional":true},{"name":"Snowflake","optional":true},{"name":"SQL","optional":true}],"status":"live","first_seen_at":"2026-11-09T05:15:42Z","employer_posted_date":"2026-11-09","last_verified_at":"2026-11-10T18:04:54Z","board_verified":true,"closed_at":null,"days_open":1,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":1},"description":"Curinos empowers financial institutions to put customers at the center of every decision. Our AI-first platform transforms proprietary data, advanced analytics and deep financial services expertise into timely recommendations - delivered right where teams work. The result: confident decisions, stronger customer relationships, and lasting, profitable growth.\nCurinos operates under a hybrid modality and has office locations in New York, Chicago, Boston, Toronto, and London. This role is open to hybrid/ remote candidates based in the United States.\nJob Description\nWe are seeking a Senior Product Manager to lead the Data Platform pod and own the vision, roadmap and outcomes for the full Curinos Lakehouse on Databricks. The scope runs from ingestion and data modeling through governance and data products and includes shared enterprise data assets such as canonical entities and the Customer 360 data layer, as well as common data platform tooling. This role includes people leadership of one direct report focused on methodology and semantic governance.\nThe pod's mandate is to own the Curinos Lakehouse end to end: the shared foundation where Curinos data is ingested, modeled, governed and made available to products, analytics and AI. A key priority within that mandate is expanding the breadth and depth of the Customer 360 data layer. The Data Platform pod maximizes the value of Curinos data by bringing datasets together across products, modeling data centrally, and making that data discoverable, governed and AI-ready, always within the permissions set by our data-use policies. You will also partner closely with our Head of Data Strategy, who leads the expansion of our strategic data assets through external partnerships and data acquisition, including proofs of concept (POCs) for new data assets that may involve hands-on discovery.\nThis is a platform product management role. Your customers are the product pods, advisory and analytics teams, and our AI platform. Success requires a deep understanding of Databricks, a real appreciation for how a well-governed data platform powers a robust, Databricks-native AI platform, from metadata and semantic layers to agents and MCP-enabled data discovery, and the comfort to get hands-on with the data yourself.\nResponsibilities\nOwn the Data Platform vision, roadmap and quarterly outcomes across the full Databricks Lakehouse, from ingestion and modeling to governance, data products and shared enterprise data assets, including the Customer 360 data layer\nBring data from across pods together into trusted, well-modeled data products across business domains, including Customer 360 gold assets, with a central catalog that pods and decisioning use cases can draw from directly\nMake data AI-ready by default through data contracts, complete metadata, in-pipeline data quality checks and governed semantic layers\nPartner with the AI Platform, App & API Platform, product pods, Data Strategy, Engineering and Advisory to define reusable patterns that pods can adopt and build on\nLead and develop one direct report focused on methodology and semantic governance\nPartner with the Head of Data Strategy on POCs for new data assets, rolling up your sleeves for hands-on discovery where it helps the team move faster\nEnsure Databricks operations are well managed at scale, including compute, permissions, cost allocation and compliance, partnering with the teams that run the platform day to day so operations never slow delivery\nPartner with Databricks to align the enterprise data platform strategy and maturity roadmap with industry direction and Databricks platform evolution\nStrategic Vision\nOwn the product vision and roadmap for the full Data Platform: the Databricks Lakehouse end to end, including the Customer 360 data layer, and shared data tooling. Prioritize investments against clear strategic pillars: powering AI-ready decisioning, powering customer-level decisioning, centralizing institutional knowledge and ensuring Databricks operations scale reliably. Adopt native Databricks capabilities where they offer long-term value, reducing custom engineering over time. Look beyond the Data Platform pod to the broader suite of Curinos products, the AI Platform and the future value our data can drive, and shape the roadmap with that bigger picture in mind.\nBusiness Value and Platform Economics\nDrive platform decisions with strong business and financial acumen, balancing scalability, reliability and cost efficiency while demonstrating value through accelerated time-to-value, adoption, operational excellence and measurable business impact. Make the economics of each platform investment clear and define how success is measured for every data platform product, so trade-offs and outcomes are visible to stakeholders.\nLakehouse Ownership\nOwn the Curinos Lakehouse end to end as a product, including ingestion, the bronze, silver and gold layers, data modeling standards and shared, domain-based data products that serve every Curinos One product, Advisory and analytics use case. Set the standards for how data lands, is modeled and is promoted to production, so new data sources and use cases can be added quickly and consistently. Make sure the Lakehouse serves established analytics and benchmarking workloads as well as newer ones, such as customer-level decision making and AI.\nCustomer 360 Data Layer\nAs a key part of the Lakehouse, grow the breadth and depth of the Customer 360 data layer by bringing Curinos datasets together into a unified, customer-level view, in line with client agreements and data-use permissions. Define and run a repeatable intake and operating model for Customer 360: when new assets should be added, how they are prioritized and how they are governed. Partner with the Head of Data Strategy to integrate client-contributed and third-party data, and with business, legal, compliance and risk leaders to make sure identity, linkage, privacy and data-use controls protect client confidentiality and trust.\nNew Data Asset Discovery and POCs\nPartner with the Head of Data Strategy on POCs for new data assets, such as client-contributed, partner and third-party data. Get hands-on in discovery where applicable: explore sample data, test how well it links to existing Curinos and Customer 360 data, prototype in Databricks and size the potential value for Curinos products and AI use cases. Once data-use rights, privacy and legal reviews are complete, turn successful POCs into production-ready Lakehouse assets with clear owners, contracts and a path onto the roadmap.\nInternal Customer and Pod Enablement\nTreat product pods, Advisory and analytics teams as your customers. Run discovery with Curinos One product managers on how data use cases are identified and delivered, promote shared aggregation and pipeline patterns, and organize data by business domain rather than by product so teams can easily find and reuse what already exists. Help lead cross-team data architecture reviews for cross-pod data requests and new Customer 360, Curinos One and Copilot use cases.\nAI-Ready Data Foundation\nPartner closely with the AI Platform team to make Curinos data ready to power models, agents and AI-assisted experiences, with the same governance and access controls that apply across the Lakehouse. Work with your direct report focused on methodology and semantic governance to make sure governed semantic layers keep business meaning in the platform rather than in prompts, improving accuracy and reducing the cost of AI interactions. Expose governed data products, semantic assets and metadata, including Customer 360 and Lakehouse assets, through standardized interfaces that models, agents and MCP-enabled experiences can safely consume, and help discover and manage the AI artifacts built on them, from natural-language analytics to agentic use cases across Curinos One.\nGovernance, Contracts and Data Quality\nMake machine-readable data contracts based on the Open Data Contract Standard, complete metadata and in-pipeline data quality checks a standard part of how data is promoted to production. Continue to grow the number of documented and certified assets in Unity Catalog, with clear human ownership and certification, lineage and access controls at every layer of the Lakehouse.\nDatabricks Platform Operations\nEnsure Databricks runs reliably as usage grows across pods, partnering with the teams that handle day-to-day platform operations to make sure compute management, project and workspace setup, permissions and access requests, catalog structure, cost allocation and compliance controls are well managed. Stay current on the Databricks roadmap, make room for proactive exploration of new capabilities, and translate what you learn into a unified Data and AI roadmap.\nPerformance and Learning\nDefine and track product success measures for the platform, such as time-to-value for new data use cases, adoption and reuse of shared assets, metadata and contract completeness, certified data products, use cases delivered across the lakehouse and enterprise data assets, platform reliability and cost efficiency. Use these measures, stakeholder feedback and adoption data to show progress against quarterly goals, quantify business impact and guide future investment.\nPeople Leadership\nLead and develop one direct report focused on methodology and semantic governance, helping make sure Curinos data is calculated consistently and defined the same way across products and AI.\nSalary Range: $160,000- $170,000 USD (plus Bonus)\nDesired Skills & Expertise\n6+ years of Product Management experience, including 3+ years of owning data platform, data infrastructure or analytics platform products\nDeep, hands-on understanding of Databricks, including Lakehouse architecture, Unity Catalog, Delta Lake, Databricks SQL, Lake flow pipelines and jobs, and compute and cost management\nStrong working 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\nExperience with data governance at scale, including catalogs, metadata, lineage, access controls, data contracts and data quality frameworks\nProven ability to run a platform as a product for internal customers: driving adoption of shared patterns, balancing central standards with pod autonomy and making clear prioritization trade-offs\nExperience with customer-level or entity-level data, including identity resolution, data linkage and handling sensitive data in a regulated environment\nStrong 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\nDemonstrated experience as a strong individual contributor who is comfortable being hands-on with the data: writing SQL, exploring and profiling datasets in Databricks notebooks, checking data quality and linkage, and prototyping quickly to validate ideas before engaging engineering\nAbility to think beyond a single pod to the broader product suite, the AI platform and the future value data can drive across the business.\nStrong communication skills and demonstrated ability to explain complex data and AI concepts to executives and to technical and non-technical stakeholders\nExperience managing, developing, and/or mentoring product managers\nStrong judgment, ownership and follow-through, with the ability to manage multiple workstreams, own quarterly outcomes and make practical trade-offs\nPreferred Skills & Expertise\nExperience in financial services, fintech, banking or another regulated industry\nDatabricks certifications, such as Data Engineer or Generative AI Engineer\nExperience with semantic layers, metrics stores, business catalogs or ontologies\nExperience with third-party data, identity spines, clean rooms or zero-copy data sharing such as Delta Sharing\nExperience with other modern data platforms such 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