Marketing Measurement Data Manager

LinkedIn•Sunnyvale, CA

2d•Hybrid

About The Position

LinkedIn is the world’s largest professional network, built to create economic opportunity for every member of the global workforce. Our products help people make powerful connections, discover exciting opportunities, build necessary skills, and gain valuable insights every day. We’re also committed to providing transformational opportunities for our own employees by investing in their growth. We aspire to create a culture that’s built on trust, care, inclusion, and fun – where everyone can succeed. Join us to transform the way the world works. Job Description At LinkedIn, our approach to flexible work is centered on trust and optimized for culture, connection, clarity, and the evolving needs of our business. The work location of this role is hybrid, meaning it will be performed both from home and from a LinkedIn office on select days, as determined by the business needs of the team. This role can be based in our New York City, San Francisco, Sunnyvale, Mountain View or Chicago office. LinkedIn's Marketing Measurement team uses Marketing Mix Modeling (MMM) to measure campaign effectiveness and optimize investment decisions across multiple business lines and countries. This role supports the data infrastructure that makes MMM possible - managing data preparation, collection, and validation workflows while ensuring data integrity throughout our MMM program transformation from quarterly to monthly cadence. You'll partner with senior team members, marketing analytics, data engineering, and vendor partners to execute data operations for MMM refreshes across US, UK, and international markets.

Requirements

BA/BS degree in Finance, Marketing, Data Science, Statistics, STEM, or equivalent experience.
4+ years of experience in marketing analytics, data analytics, or a related role
3+ years in a data analytics role with SQL and Python or similar data analysis tools and programming languages

Nice To Haves

Experience supporting marketing measurement programs, such as Marketing Mix Modeling (MMM) or attribution
Experience managing recurring data collection and validation workflows across multiple data sources
Experience diagnosing data quality issues and coordinating fixes with technical and non-technical partners
Familiarity with marketing data concepts such as campaign metrics, media mix, or conversions
Experience working with automated data pipelines or QA checks
Comfort operating in fast-moving environments with recurring deadlines
SQL-based data analysis and validation
Marketing data collection and QA
Marketing measurement data support (including MMM inputs)
Data workflow coordination across systems and partners
Stakeholder and program coordination

Responsibilities

Data Preparation, Collection & Pipeline Management Manage end-to-end data collection from MMM sources including Agencies, Singular/Campaign Central, DataWatch, and Fawkes
Execute data egress and transfer workflows to vendor partners for: Monthly LMS US models Quarterly models across LMS UK, Consumer US/UK, LSS US/UK, LTS US/UK, Parent Brand US/UK
Validate and reconcile data during platform transitions
Lead investigation and integration of new datasets to enhance MMM models
Explore and identify data availability to model additional countries or business units
Data Validation & Quality Assurance Execute validation frameworks that check channel-level inputs, KPI availability, data completeness, format accuracy, and historical variance/anomaly detection
Partner with data engineering, data operations, and vendors on UAT requirements and data cuts for MMM models and re-models
Conduct detailed comparisons to ensure historical and future conversion figures remain reliable
Diagnose data quality issues during validation workflows and coordinate resolution with source system owners
Develop and maintain standardized processes and documentation for data QA
Support quarterly validation workflows to reduce cycle time through pipeline-based checks
Process Improvement & Automation Identify opportunities to streamline data collection, validation, and reporting processes
Partner with GTMDS and data engineering teams on automation priorities for quality checks, egress, and ingestion workflows
Document processes and build playbooks that reduce manual interventions
Stakeholder Coordination Serve as key point of contact for data dependencies across GPO and Agency partners on MMM initiatives
Coordinate quarterly and monthly modeling cycles across internal teams and external vendors
Maintain clear communication on data timelines, dependencies, and blockers
Collaborate with analytics and data engineering teams to resolve data issues (note: data engineering support provided by partner team)