Sr Data Engineer

About the Role

We're looking for a Senior Data Engineer that is innovative, curious, and collaborative to help evolve the data platform that powers our sell-side media business. This role supports data-driven workflows and a broad range of media monetization use cases, with an emphasis on scalable, open data architectures.

You'll work on high-volume, revenue-critical media data while helping modernize how data is stored, processed, and served across the organization. This is a hands-on role for engineers who enjoy building, migrating, and improving platforms, not just maintaining them.

What You'll Do

• Design, build, and maintain scalable, reliable data pipelines that support sell-side media monetization.
• Ingest, integrate, transform, and model high-volume media and campaign data from multiple sources, delivering analytics-ready datasets that meet quality, accessibility, and business requirements.
• Develop lakehouse-style data models that balance flexibility, performance, and cost efficiency, enabling reporting, analytics, operational workflows, and downstream consumption.
• Build, optimize, and maintain ETL/ELT processes for both batch and near-real-time workloads, orchestrated with modern workflow tools (e.g., Airflow, Dagster).
• Collaborate with machine learning, client-facing, product, application, and analytics teams to understand requirements, support ML feature productionization, and enable self-service insights.
• Ensure data quality, lineage, observability, governance, and security, safeguarding cloud data assets and maintaining trust in revenue-critical systems.
• Contribute to platform evolution and migration efforts, including evaluating tools, improving workflows, and reducing architectural complexity.
• Continuously optimize pipelines, queries, and storage for performance, scalability, and cost efficiency.
• Mentor junior engineers and participate in technical design and architecture reviews.
• Detect, investigate, and resolve data anomalies to maintain pipeline reliability and data trustworthiness.

Required Experience & Skills

Core Requirements

• 5+ years of experience in building highly scalable and reliable data engineering and analytics platforms
• Strong experience building and optimizing modern data pipelines, architectures, and datasets using Data Lake, Data Warehouse, and Lakehouse paradigms
• Advanced SQL, Python, and PySpark skills and experience building analytics-ready data models
• Experience using Iceberg, Delta, and Parquet data formats
• Experience using Dagster and Airflow orchestration tools
• 2+ years hands-on experience with cloud platforms, such as GCP & AWS
• Experience designing and building data pipelines on object storage-based platforms (e.g., S3, Wasabi, Dremio, Snowflake, Delta Lake) to process and manage large-scale analytical datasets
• Solid understanding of data quality, testing, monitoring, and operational reliability
• Strong communication skills and ability to work closely with technical and non-technical stakeholders
• Experience collaborating with Software Engineers using Agile methodologies to build web applications that access, visualize, and sometimes update big data stores in a hybrid OLAP and OLTP environment.
• Bachelor's degree or equivalent work experience (minimum 5 years) in Computer Science or related field

Preferred/Nice-to-Have

• Experience in media industry strongly preferred including familiarity with sell-side concepts (linear TV or digital inventory, campaign delivery, pacing, audience-based selling and measurement)
• 1+ years experience supporting or leading data platform migrations, hybrid architectures, or warehouse-to-lakehouse transitions using Dremio is a strong plus
• Experience with Tableau, Metabase, or other data visualization tools
• Experience working in an operational environment with time-sensitive customer commitments