Senior Data Engineer, Ads Data (AI Native)

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Data Engineer, Ads Data (AI Native) based in the United States.

Join a fast-growing, remote-first technology organization dedicated to helping families stay connected and protected through innovative digital products and services. As a Senior Data Engineer, you will take ownership of critical data infrastructure supporting advertising analytics, campaign performance, and business intelligence. You will design, optimize, and scale data pipelines that transform billions of data points into reliable, actionable insights. Working closely with analysts, advertising strategists, and sales teams, you will help create trusted datasets and standardized metrics that drive smarter business decisions. You will leverage modern cloud technologies and AI-powered development tools to improve engineering efficiency, platform reliability, and scalability. This is an opportunity to combine deep technical expertise, cross-functional collaboration, and AI-native innovation while making a meaningful impact within a globally

Accountabilities

• Build and maintain scalable data infrastructure: Enhance, extend, and operate batch and streaming pipelines, Delta Lake tables, and data workflows on Databricks to support growing data volumes and evolving business requirements.

• Develop business-focused data products: Collaborate with business analysts, advertising strategists, and sales teams to translate their needs into reliable datasets, campaign performance reports, audience insights, and revenue analytics.

• Own data modeling and metrics: Build and maintain dbt models, automated tests, curated Silver and Gold datasets, and semantic layers to ensure consistent and trusted business metrics across the organization.

• Optimize performance and infrastructure costs: Improve Spark and Databricks workloads through query optimization, partitioning, cluster sizing, shuffle management, and efficient resource allocation.

• Manage cloud infrastructure: Maintain and automate Databricks and AWS infrastructure using Terraform, ensuring secure, scalable, and reliable data platform operations.

• Integrate multiple data sources: Connect, consolidate, and reconcile data from advertising platforms, CRM systems, external partners, and internal applications while supporting migrations from legacy reporting solutions.

• Strengthen data quality and reliability: Implement monitoring, data lineage, SLAs, alerting, automated testing, and data contracts. Lead root-cause analysis and resolve issues affecting data availability and accuracy.

• Establish engineering best practices: Promote effective version control, CI/CD, code reviews, testing, and documentation while mentoring engineers and contributing to the team's technical roadmap.

• Ensure data governance and compliance: Apply appropriate privacy controls, security standards, and access management practices to protect sensitive information and meet GDPR, CCPA, and SOX requirements.

• Advance AI-native engineering capabilities: Build tools, workflows, and AI agents that provide LLMs with additional context, helping accelerate development, debugging, code generation, and system maintenance.

• Drive continuous improvement: Take ownership of foundational pipelines and data models, reduce processing times and infrastructure costs, and enable stakeholders to access trusted information independently.

Requirements

• At least 5 years of professional data engineering experience, with a proven track record of building and operating production-grade data platforms at enterprise scale.

• Advanced hands-on expertise in Apache Spark, including PySpark and/or Scala, with strong knowledge of partitioning,