The candidate should be comfortable working with large-scale marketing datasets and translating advertising and campaign data into measurable business insights while operating within privacy and clean-room constraints.
Develop and analyze advertising measurement solutions covering reach and frequency, campaign pacing, attribution, incrementality, media effectiveness, and audience performance.
Build analytical queries and data models using advanced SQL and Python to support marketing and advertising measurement use cases.
Design and implement measurement approaches for multi-touch attribution (MTA), marketing attribution, campaign performance, and media mix modeling (MMM).
Analyze advertising campaigns across digital media channels and evaluate campaign reach, engagement, conversion, and effectiveness.
Work with advertising ecosystems such as Meta, Google DV360, Google Marketing Platform, TikTok, Pinterest, Amazon Advertising, and similar platforms.
Develop measurement solutions within privacy-safe data collaboration and clean-room environments.
Work with platforms such as LiveRamp Clean Room/Safe Haven/Habu, Amazon Marketing Cloud, Amazon Clean Rooms, Google Ads Data Hub, GMP, Snowflake, or comparable technologies.
Create clean-room analytical logic, including questions, queries, UDFs, and privacy-compliant analytical workflows within publisher or platform constraints.
Apply privacy-safe aggregation techniques, including minimum group-size requirements, suppression rules, noise controls, and restricted data-access patterns.
Support identity resolution use cases involving deterministic and probabilistic matching, persistent identifiers, RampID or equivalent IDs, and match-rate optimization.
Build and maintain scalable data pipelines across AWS, Azure, or Google Cloud Platform environments.
Use Python and/or Spark for data transformation, analytical processing, and pipeline development.
Partner with marketing, media, analytics, data engineering, and business teams to translate measurement requirements into technical solutions.
Validate analytical results and ensure measurement methodologies are accurate, reproducible, and aligned with business objectives.
Follow applicable data privacy and regulatory requirements, including CCPA, GLBA, and applicable state privacy regulations.
Document analytical methodologies, data models, clean-room logic, and technical processes.
Troubleshoot data quality, query, pipeline, identity-matching, and measurement-related issues.
5–7+ years of experience in data science, data engineering, marketing technology, advertising analytics, identity infrastructure, or related fields.
2+ years of hands-on experience working with data clean rooms or privacy-safe data collaboration environments.
Experience developing clean-room questions, analytical queries, and/or UDFs, rather than only performing ETL or data ingestion.
Strong understanding of privacy-preserving aggregation techniques, including suppression, minimum aggregation thresholds, and noise budgets.
Experience supporting retail media, advertising agencies, consumer brands, or digital media organizations.
Experience working with organizations such as Publicis, WPP, Omnicom, IPG, Dentsu, GroupM, Walmart, Target, Ulta Beauty, Kroger, Home Depot, Lowe's, Costco, CVS, or Walgreens is a plus.
Strong communication and problem-solving skills with the ability to work across technical and marketing stakeholders.
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