Senior Competitive & Market Insights Manager

Intapp•Palo Alto, CA

4d•Hybrid

About The Position

The best competitive intelligence work doesn't just inform decisions; it shapes them. At Intapp, that means understanding the firms at the center of the world's most demanding professional work: legal, accounting, investment banking, consulting, and private capital. You'll design the architecture for how intelligence flows in, gets structured, and becomes a specific, audience-targeted recommendation that lands with the right team at the right moment. You won't be maintaining a system someone else built; you'll build the infrastructure, write the playbooks, run the analysis, and connect findings to business context, all in a single workflow, with AI-first tools as the foundation. If you're the kind of operator who sees the gap and builds the solution before being asked, this role was designed for you.

Requirements

8+ years of experience in competitive intelligence, market research, data analytics, strategy, or a closely related field, including significant experience in B2B SaaS.
Proven program ownership: you have run a function or workstream end-to-end, set its priorities, and been accountable for outcomes to senior leadership.
Intellectual curiosity about competitive dynamics, market structure, and regulated industries.
Genuine, practiced fluency with AI tools: you use large language models, AI-assisted research platforms, and automation tools as a natural part of your daily workflow.
Demonstrated track record designing and shipping data pipelines, taxonomies, and analytical frameworks from scratch, and evolving them as scope grows.
Executive-level, audience-aware communication: you can take a complex dataset and explain not just what it shows, but why it matters to a specific team and what they should do next.
Sophisticated analytical capabilities: you have moved beyond descriptive statistics and can apply techniques like segmentation, trend modeling, or signal extraction to messy real-world data.
Advanced data skills: comfortable with SQL, Python (pandas, NumPy, or similar), or equivalent tools. Able to design, clean, and query structured datasets independently.

Nice To Haves

Familiarity with B2B SaaS, legal tech, fintech, private capital, or services software markets.
Experience with competitive intelligence or voice-of-the-customer programs.
Background in business intelligence, data visualization, or dashboard development.
Experience building internal tools, automated workflows, or reporting infrastructure.
Exposure to CRM data or go-to-market analytics (Salesforce, HubSpot, or equivalent).
Bachelors Degree; MBA optional

Responsibilities

Define and own the strategy and technical architecture of the program.
Design and maintain the full data infrastructure: ingestion, storage, enrichment, and analysis using AI-first tooling throughout.
Lead cross-functional working sessions with subject matter experts to build data synthesis frameworks.
Partner with internal SMEs across product, go-to-market, and industry practices to develop structured approaches for synthesizing disparate data sources into coherent, decision-ready intelligence.
Set the standard for, and steward, program playbooks. Document repeatable workflows for sourcing, cleaning, structuring, and analyzing competitive and market data, so the program scales as scope grows.
Identify and route meaningful insights. Surface actionable intelligence from market signals, competitor movements, and industry trends and pair each finding with a clear recommendation for the right internal audience (product, sales, marketing, executive leadership, etc.), including explicit rationale for why that team should care.
Leverage AI natively across the research cycle. Use AI tools for data collection, synthesis, pattern recognition, and distribution, not as a shortcut, but as the foundation of how the program operates.
Advise and influence senior cross-functional stakeholders. Work closely with product management, go-to-market, corporate strategy, and executive teams to understand their intelligence needs and calibrate outputs accordingly.
Continuously improve the data infrastructure. Identify gaps, resolve quality issues, and evolve the architecture as the volume and complexity of inputs grows.
Develop prioritization and categorization frameworks. Build systems for ranking competitive intelligence by relevance, urgency, and intended audience, so the team always knows what to act on first.
Mentor and uplevel the broader team. Share, advocate, and coach on the next-generation working model on intelligence tradecraft, AI-first methods, and translating signals into decisions.