Senior Engineer - Ads Optimization

Who We Are:

Tick Pick is a technology company reshaping the ticket marketplace with a simple promise: no hidden fees. We build a better, more transparent experience for fans and a more efficient marketplace for live events. We’re a fast-moving team that values ownership, practical problem-solving, and measurable impact.

Tick Pick is a fast-growing technology company reshaping the secondary ticket marketplace. Through a combination of software innovation and best-in-class customer experience, we’ve saved our customers over $60 million in service fees.

Since our launch in 2011, we’ve sold over $1 billion in tickets, and for the last five years, Tick Pick has been named a Deloitte Technology Fast 500 award winner and recognized on the Inc. 5000 and Crain’s New York Business Fast 50 lists.

We’re looking for a technically strong individual to join our Data Team and partner with Growth to improve paid search performance by strengthening conversion tracking, attribution consistency, and the data pipelines that connect marketing platforms to Tick Pick’s transaction data across web and app. You’ve built systems that translate business context into better performance marketing execution, and you’re comfortable operating in the messy intersection of platform data, internal truth sets, and real-world edge cases.

This role is ideal for someone who is equal parts builder and analyst — you can engineer reliable pipelines and integrations, but you’re also fluent in how paid search actually works (bidding, match types, conversion value, attribution windows, etc.). You will work closely with Growth stakeholders and partner deeply with Engineering to ship changes that materially improve decision quality and campaign outcomes.

• 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.

• 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…