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Product Manager – Data as a Service

HCLTech

Noida, Uttar Pradesh, India

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.

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