Senior Manager, Knowledge Graph & Entity Data Platform

Career Guide

A Senior Manager, Knowledge Graph & Entity Data Platform leads teams that build and operate a shared “entity and relationships” data foundation (for example: people, companies, products, locations, and how they relate). The goal is to make data easier to find, connect, trust, and reuse across analytics, search, personalization, fraud/risk, and AI applications. This role blends platform engineering, data governance, product thinking, and people leadership.

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Key Responsibilities

Set strategy and roadmap for the entity data and knowledge graph platform (what gets built, why, and in what order).
Lead and mentor cross-functional teams (data engineers, platform engineers, data modelers, and sometimes applied ML/AI engineers).
Define the “entity model”: how core business objects (customers, products, suppliers, etc.) are represented and linked.
Oversee entity resolution (deduplicating and matching records from different sources) and data quality practices.
Design scalable data pipelines for ingesting, validating, and updating entity and relationship data.
Partner with product, analytics, security, and legal/compliance teams to ensure data is used appropriately and safely.
Establish standards for metadata, documentation, access controls, and data lifecycle management.
Ensure the platform is reliable in production: monitoring, incident response, performance tuning, and cost management.
Create adoption plans: onboarding guides, APIs/services, internal tooling, and support for downstream teams.
Measure impact using clear metrics (data quality, coverage, platform usage, latency, cost per query, time-to-integrate new sources).

Top Skills for Success

People leadership (coaching, hiring, performance management, setting team direction)

Clear communication with both technical and non-technical stakeholders

Program and roadmap management (prioritization, sequencing work, managing dependencies)

Data modeling for entities and relationships (designing consistent definitions and structures)

Entity resolution / record matching (linking duplicates and conflicting sources reliably)

Graph concepts and query patterns (how relationship data is stored and retrieved efficiently)

Data platform engineering (batch and streaming pipelines, APIs/services, scalability)

Data quality and governance (standards, audits, access controls, lineage, documentation)

Cloud and cost management (capacity planning, performance tuning, budget ownership)

Partnering with applied AI/ML teams (using entity/graph data to improve models and retrieval)

Career Progression

Can Lead To

Director, Data Platform / Data Engineering

Director, Knowledge Graph / Entity Intelligence

Head of Data Products / Data Platform

Director, Data Governance & Data Quality (in some organizations)

Transition Opportunities

Principal/Staff Platform Architect (if moving back to an individual-contributor track)

Product Director for Data/AI Platforms

Head of Search/Discovery Data or Personalization Platforms

CTO/VP Engineering path in data-heavy organizations

Common Skill Gaps

Often Missing Skills

Strong entity resolution design (match strategies, thresholds, human-in-the-loop review, error analysis)Proven approach to measuring graph/platform value (adoption, quality, business outcomes)Operational excellence at scale (SLAs, incident management, observability, cost control)Balancing governance with usability (making the platform safe without slowing teams down)Experience delivering a platform as a product (APIs, documentation, onboarding, support model)

Development SuggestionsBuild a portfolio of concrete outcomes: one or two end-to-end launches (new entity model, new matching system, or major platform migration) with measurable adoption and quality gains. Practice writing a simple platform “product brief” that clarifies users, use cases, service levels, and success metrics. Strengthen operational skills by owning reliability and cost targets for a quarter and documenting improvements.

Market Intelligence Report

Senior Manager, Knowledge Graph & Entity Data Platform is part of the Data & Platform Engineering category.Explore our market intelligence report to see how AI and hiring demand are shifting for these roles.

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Salary & Demand

Median Salary Range

Entry LevelTypically not an entry-level role; closest equivalent (Manager, Data Platform/Knowledge Graph) often ranges $160k–$210k base (US).

Mid LevelSenior Manager commonly ranges $190k–$260k base (US), depending on company size, location, and scope.

Senior LevelDirector/Head of Knowledge Graph or Entity Platform often ranges $230k–$320k+ base (US). Equity/bonus can materially increase total compensation.

Growth Trend

Growing demand. Organizations are investing in better data foundations to support AI, personalization, search, and governance. Hiring is strongest in large tech, financial services, e-commerce/marketplaces, and data-intensive enterprises modernizing their data platforms.

Companies Hiring

Major Employers

GoogleMicrosoftAmazonMetaAppleNetflixLinkedInUberAirbnbStripePayPalSalesforceSnowflakeDatabricksPalantirBloombergThomson ReutersJPMorgan ChaseGoldman Sachs

Industry Sectors

Big Tech and consumer internet platformsFinancial services (banking, payments, capital markets)E-commerce and marketplacesB2B SaaS and data infrastructure vendorsMedia, publishing, and information servicesHealthcare and life sciences (patient/provider/entity master data)Telecommunications and cybersecurity (identity/entity intelligence)

Recommended Next Steps

Clarify your target scope: are you leading the core graph store, the entity resolution layer, or the full entity platform (pipelines + APIs + governance)? Tailor your resume accordingly.

Create a one-page case study from past work: problem, approach, team size, architecture at a high level, and metrics (quality, latency, cost, adoption).

Benchmark your skill set against job descriptions: graph data storage/querying, entity matching, platform operations, and data governance; pick 1–2 gaps to close.

Prepare interview stories for: stakeholder alignment, tough prioritization calls, incident/production issue leadership, and improving data quality over time.

Build or refresh hands-on familiarity with modern data tooling (cloud data platforms, orchestration, monitoring). You don’t need to be the deepest engineer, but you should credibly review designs and tradeoffs.

Network with adjacent leaders (Search/Discovery, Personalization, Fraud/Risk, Data Governance) to understand where entity/graph platforms create immediate business value.