Job Description
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Company Overview
A rapidly growing technology company is transforming the television advertising industry through the intelligent application of artificial intelligence, machine learning, sophisticated media buying, and proprietary analytics. Its platform combines advanced advertising technology with data-driven measurement capabilities to create an automated, digital-like experience for businesses advertising across linear and streaming television.
The organization works with high-growth brands across a variety of industries and has received recognition from leading industry organizations for innovation in connected TV, advertising technology, and media technology. Backed by experienced technology and business leaders, the company continues to expand as television advertising enters a new era of data-driven and automated solutions.
The Senior Data Engineer will join the Reporting and Measurement pillar and will be responsible for designing, building, and owning large-scale data pipelines that transform high-volume raw data into curated, production-grade datasets. These datasets will power reporting and measurement products used across the organization.
The role involves taking data products from initial requirements through technical specifications, implementation, validation, and production rollout. The successful candidate will work closely with data science teams on methodology, backend and frontend engineers on serving layers, and product teams on requirements.
Key Responsibilities
- Design, build, and operate large-scale production data pipelines.
- Transform high-volume raw data into curated, production-grade datasets.
- Own data products from requirements gathering and technical specifications through implementation, validation, and production deployment.
- Partner with data science teams to establish and validate data methodologies.
- Collaborate with backend and frontend engineers to develop effective data-serving layers.
- Work closely with product teams to understand requirements and translate them into reliable data solutions.
- Develop and maintain analytics-focused data models and curated datasets.
- Implement robust validation, reconciliation, and data-quality processes.
- Investigate and resolve issues across distributed data systems.
- Optimize data pipelines and processing workloads for performance, reliability, and scalability.
- Design and manage workflows, including backfills and failure recovery.
- Create clear technical specifications and incident documentation.
- Evaluate emerging agentic developer workflows and explore how AI-powered development paradigms can improve software engineering practices.
- Contribute to a culture of continuous learning, technical excellence, and cross-functional collaboration.
Required Qualifications
- 5+ years of dedicated experience designing, building, and operating production ETL pipelines at scale.
- Strong programming skills in Python or a similar programming language.
- Advanced SQL skills.
- Deep hands-on experience with Apache Spark or PySpark.
- Experience working with Databricks and Delta Lake.
- Experience with workflow orchestration tools such as Apache Airflow.
- Strong knowledge of DAG design, backfills, and failure recovery.
- Strong data modeling skills for analytics applications.
- Experience designing curated or gold-layer datasets.
- Understanding of schema design, partitioning, and data contracts consumed by downstream services.
- Strong understanding of data quality, validation, reconciliation, and methodology comparison.
- Experience debugging distributed data systems, including performance tuning, memory issues, and system optimization.
- Excellent written communication skills.
- Strong ability to collaborate effectively with data science, engineering, and product teams.
Preferred Qualifications
- Experience with OLAP data stores such as ClickHouse.
- Experience developing or testing agentic developer workflows.
- Familiarity with agent harnesses and agent loop engineering.
- Interest in emerging AI-assisted software development methodologies.
- Experience working with large-scale reporting and measurement datasets.
- Experience owning data products throughout their complete development and production lifecycle.
Disclaimer
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