Product Manager Data as a Service

Role: Principal / Lead Product Manager – Data as a Service

Location: Noida, India

Experience Level: 13–19+ years total experience (minimum 6+ years in Product Management with prior experience in Data Management)

Role Summary

We are seeking a visionary, technically strong, and outcome-oriented Product Manager – Data as a Service to spearhead the evolution of our enterprise data ecosystem. In this role, the person will lead the shift from traditional, centralized data pipelines to Data as a Service—treating internal and external data assets, semantic models, feature stores, and analytical views as high-value, discoverable, consumable, and SLA-governed AI Native Data Services.

As Product Manager, you will not simply automate existing analytical workflows; your role will be to completely reimagine the end-to-end organization experience of data discovery, consumption, governance, and exploration through Classic, Generative and Agentic AI.

You must possess a strong blend of technical understanding of Data Offerings and Product Management excellence within established software/SaaS product companies, having guided complex platforms from concept to widespread enterprise adoption

Key Responsibilities & Core Focus Areas

A. Strategic Product Definition & Specification Lifecycle (MRD, PRD, FSRD)

• Overall Revenues and P&L: As the Product Leader for Data-as-a-Service, you will manage the overall revenue numbers from the offering, including profitability, customer adoption and technology frameworks.
• Define the Strategy for our offering, including market potential, HCLTech Pulse play, market analysis (including customer and competition), productization, packaging and life cycle management
• Market Needs Analysis: Conduct quantitative market sizing, competitive benchmarking, user persona segmentation (Cross-functional CXOs, including CDOs, CIOs, CDAO and others), and identify market opportunities for Data-as-a-Service monetization and operational acceleration.
• Product Requirements Document (PRD): Define high-impact product roadmaps, problem statements, user journeys, measurable acceptance criteria, and success metrics (SLAs, SLOs, TTVs – Time to Value). Embed enterprise grade security, controls, lineage tracking and regulatory compliance as default.
• Functional & System Requirements Document (FSRD): Author granular functional specifications, interface definitions, API/SDK designs, schema contracts, event stream definitions, metadata topologies, and system integration workflows in close collaboration with Lead Data Architects and Engineering Managers.
• Product Discovery & Evangelism: Lead iterative customer/internal discovery engagements, alpha/beta pilot programs, user feedback synthesis, and launch enablement.
• Manage Data Stack Complexity and Governance: Ensure that the overall offering adheres to various customer segment / solution KPIs, including data management and quality, compliance, governance and transparency.
• AI-Native Product Innovation: Deliver balance between scale, costs and performance. In addition, eliminate traditional SQL / Dashboard friction by introducing conversational discovery, visual lineage graphs, multi-modal semantic search, proactive alerting, and autonomous exploratory data analysis (EDA).

Candidate Profile & Qualifications

Required Experience

• Total Professional Experience: 12 to 19+ years of demonstrable success in Product management, Data & AI technology.
• Product Management Experience: Minimum 6+ years of formal Product Management experience within modern product-centric software companies (SaaS/Enterprise B2B/Data Platform companies).
• Technical Heritage in Data Management: Proven track record having worked hands-on as a Data Engineer, Big Data Architect, or Software Engineer in early career stages. Deep foundational empathy for data pipeline bottlenecks, schema migrations, orchestration, and compute/storage tradeoffs.
• AI-Native Product Track Record: Prior experience designing, launching, and scaling customer facing products incorporating AI/ML capabilities, specifically including Generative AI and Agentic systems.

Preferred & Good to Have Technology Experience

Strong practical familiarity with the following platforms and toolchains is highly advantageous:

• Cloud Platforms (Hyperscalers): Proven experience operating in or building products on Google Cloud Platform (GCP), Microsoft Azure, or Amazon Web Services (AWS).
• Modern Cloud Data Warehouses & Lakehouses: In-depth knowledge of Databricks (Lakehouse, Unity Catalog, Delta Live Tables), Snowflake (Data Cloud, Snowpark, Cortex), and Google BigQuery (GBQ).
• Enterprise AI, LLMs & Foundation Models: Experience leveraging managed AI platforms such as Google Cloud Vertex AI, foundational model families including Gemini (Pro, Flash) and open-weights Large Language Models (LLMs), as well as enterprise LLMOps, vector search, and fine-tuning ecosystems.

Soft Skills & Leadership Attributes

• Executive Presence & Cross-Functional Influence: Ability to align C-level executives, Data Scientists, and Business Executives on a unified data vision.
• First-Principles Problem Solver: Obsessed with breaking down opaque, chaotic data architectures into modular, elegant, productized services.
• Customer-Centric UX Sensibility: Instinctive drive to shield users from underlying architectural complexity through intuitive, delight-driven interface paradigms.