Build and own data products that support paid search and performance marketing decision-making by translating internal transaction and catalog context into scalable reporting and operational tooling.
Develop maintainable enrichment pipelines that incorporate key catalog attributes and business context needed for consistent measurement, reporting, and operational workflows.
Partner with Growth to define and operationalize intent and catalog groupings that improve account organization and reduce irrelevant coverage, including tooling to monitor query coverage, relevance, and drift over time.
Create repeatable analytics that help Growth allocate spend toward the highest-opportunity areas, including frameworks for evaluating performance by intent/campaign grouping, lag/cohort behavior, and customer segmentation.
Engineer production-grade pipelines that connect marketing platform data with internal outcomes, enabling consistent conversion reporting, enrichment, backfills, and monitoring across web and app where applicable.
Collaborate closely with Engineering to implement instrumentation and data contracts that keep marketing measurement stable, auditable, and reliable as products and tracking evolve.
Drive cross-functional execution: write specs, define success metrics, break work into tickets, and deliver improvements that materially change how campaigns are managed and optimized.

Requirements

4–8+ years in a technical role (data engineering, analytics engineering, applied data science, marketing engineering) delivering production systems that influence business outcomes.
Expert SQL and strong Python (or equivalent); experience building operational data products (pipelines, heuristics, monitoring, QA), not just dashboards.
Demonstrated experience using internal datasets (catalog/inventory/customer behavior) to support operational decision-making in a growth, marketplace, or e-commerce context.
Experience working with performance marketing or acquisition data; understanding how coverage, relevance, and measurement quality affect outcomes.
Proven ability to join platform data to internal outcomes (UTMs/click identifiers/user/order IDs), handle edge cases, and maintain data integrity over time.
Experience building “at-scale” grouping/classification logic (rules/taxonomies/heuristics; advanced methods where appropriate) to organize messy high-volume inputs (e.g., queries, catalog entities).
Familiarity with marketing platform tooling and APIs (Google Ads experience a plus).
Ability to work in our NYC hybrid / in-office model.

Benefits

A hybrid in-office approach, enabling remote work a portion of each week
Health Care Plan (Medical, Dental & Vision)
Retirement Plan Contribution (401k, IRA)
Life Insurance (Basic, Voluntary & AD&D)
Paid Time Off (Vacation, Sick & Holidays)
Family Leave (Maternity, Paternity)
Training & Development
$100 Monthly Stipend to Attend Live Events
Employee Outings
Free Lunch & Snacks

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

SQLPythondata engineeringanalytics engineeringapplied data sciencemarketing engineeringdata productsenrichment pipelinesperformance marketingdata integrity

Soft Skills

collaborationcross-functional executionspec writingdefining success metricsorganizational skillscommunication