Senior Data Engineer, Ads Data (AI Native)

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.

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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.

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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.

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

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

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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.

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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.

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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.

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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.

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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.

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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

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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.

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Advanced hands-on expertise in Apache Spark , including PySpark and/or Scala, with strong knowledge of partitioning, joins, shuffle behavior, caching, skew handling, and performance optimization.

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Extensive experience with Databricks , including Delta Lake, Unity Catalog, Jobs and Workflows, Databricks Asset Bundles, Structured Streaming, and SQL Warehouses.

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Strong knowledge of Databricks performance and cost optimization, including cluster and warehouse sizing, autoscaling, Photon, and serverless computing.

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Excellent proficiency in SQL and Python or Scala for pipeline development, automation, data transformation, and query optimization.

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Hands-on production experience with dbt , including model design, incremental strategies, testing, documentation, and CI/CD-driven workflows.

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Practical experience with Terraform and AWS , particularly S3, IAM, and cloud networking fundamentals relevant to data platforms.

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Experience with orchestration frameworks such as Airflow or Databricks Workflows, alongside Git, CI/CD, and production monitoring tools.

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Strong understanding of data architecture and modeling principles, including dimensional modeling, star schemas, medallion architecture, and semantic layers.

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Demonstrated ability to inherit, refactor, and improve existing data models and pipelines while maintaining reliability, scalability, maintainability, and backward compatibility.

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Experience using AI/LLM coding assistants such as Claude, Cursor, or GitHub Copilot , with the ability to critically review, test, and take ownership of generated code.

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Strong analytical and problem-solving abilities, combined with business acumen and the capacity to translate stakeholder requirements into effective technical solutions.

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Excellent communication and collaboration skills, particularly when explaining technical concepts to non-technical audiences and working with cross-functional teams.

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Familiarity with data governance, privacy, and regulatory requirements, including GDPR, CCPA, and SOX.

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Experience with business intelligence platforms such as Domo, Tableau, or Looker is an advantage.

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Previous experience in advertising technology, ad tech, or media data, including campaign delivery, attribution, audience analytics, or revenue reporting, is preferred.

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A bachelor's degree in Computer Science, Engineering, or a related discipline, or equivalent professional experience.

AI-Native Engineering Expectations:

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Regularly use AI coding assistants for substantive engineering activities, including analysis, development, refactoring, testing, codebase navigation, and documentation.

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Apply sound technical judgment when reviewing AI-generated code, maintaining responsibility for quality, security, testing, and production readiness.

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Leverage AI tools to increase engineering velocity, improve efficiency, and independently tackle complex technical challenges.

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Share effective AI workflows and strategies with colleagues, helping strengthen adoption and technical capabilities across the team.

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Stay informed about emerging AI technologies and continuously identify opportunities to improve engineering processes and productivity.

Benefits

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Competitive compensation:

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Annual base salary range: $129,000–$192,000 USD .

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Equity compensation through Restricted Stock Units (RSUs).

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Comprehensive total compensation package, with final offers based on experience, skills, and geographic location.

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Health and insurance:

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Medical, dental, and vision insurance, with premiums fully covered for US employees.

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Life and disability insurance.

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Additional supplemental coverage options.

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Financial benefits:

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401(k) retirement savings plan with company matching.

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Work-life balance:

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Flexible paid time off.

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Company-wide holidays throughout the year.

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Summer and winter company shutdowns.

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Paid parental leave.

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Well-being:

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Mental Wellness Program.

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Employee Assistance Program (EAP).

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Remote work:

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Remote-first working environment.

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Equipment, tools, and reimbursement support to create a productive home office.

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Professional development:

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Learning and development programs.

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Opportunities to work with advanced data engineering technologies and cutting-edge AI tools.

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Additional perks:

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Complimentary premium membership to the company's family safety platform.

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Complimentary Tile tracking devices.

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Company culture: Join a collaborative, mission-driven, and inclusive environment that values integrity, direct communication, innovation, and meaningful impact.

How Jobgether works: We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team. We appreciate your interest and wish you the best! Why Apply Through Jobgether?

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