Top Tech Jobs & Startup Jobs

Leads Antenna’s insights and analytics function, setting editorial standards, managing custom data delivery, building internal tools and workflows, defining KPIs, and hiring and developing a remote analyst team. The role presents findings to senior clients, partners cross-functionally on data products, identifies new analytics opportunities, and remains hands-on with complex analyses. Requires strong leadership, communication, technical SQL and AI knowledge, client engagement, and media industry experience.

Owns the reliability, release, and customer-facing accuracy of core data products. Partners with Product, Data Science, Engineering, and Commercial teams to manage releases, improve data quality, monitor pipelines, investigate root causes, and deliver scalable solutions. The role requires advanced SQL, dbt, Excel, business intelligence, ETL, AI workflow optimization, stakeholder management, and mentoring experience.

Build and maintain scalable, reliable data pipelines on Google Cloud using BigQuery, dbt, PySpark, Parquet, and Dagster. Partner with Data Science and Data Operations to develop trusted data models and datasets, define requirements, improve automation and testing, document complex systems, and own data delivery reliability. The role also involves stakeholder communication and may include backend services, infrastructure as code, data governance, and applied machine learning.

Own commercial operations across deal desk, pricing, contracting, pipeline hygiene, forecasting, OKRs, CRM data, AI-driven workflows, account planning, and leadership reporting. Partner cross-functionally with Sales, Customer Success, Marketing, and Legal to standardize commercial processes, improve data quality, manage complex deals, and surface actionable business insights. The role also supports sales materials and recurring pipeline and forecast reviews.

Own reliability, release, and customer-facing correctness of Antenna's core data products. Partner with commercial teams to design data solutions, manage end-to-end product releases, build proactive data-quality monitoring, perform root-cause analyses, and deploy strategic improvements to maximize data quality and business impact. Mentor teammates and align Data Ops with customer objectives.