Sr. Software Engineer, Data Engineering

The Data Engineering team at Raptive builds and operates the core pipelines, models, and infrastructure that power data-driven decision-making across the company. From revenue reporting and marketing attribution to finance forecasting and creator performance analytics, we enable teams to work with clean, reliable, and timely data at scale.

We are looking for a Senior Data Engineer with strong experience in building reliable pipelines and modeling data to support cross-functional business needs. In this role, you’ll design and implement scalable data workflows that power decision-making across teams like Sales, Finance, Analytics, Monetization, and Marketing. This is a hands-on role focused on delivering high-quality, well-modeled data with direct impact on business outcomes.

This is a senior-level position. You’ll be expected to ramp quickly, work independently, mentor others, and help shape the technical direction of the team through thought leadership and hands-on engineering.

What you’ll be doing:

• Design, build, and maintain scalable, efficient, and reliable batch pipelines using Airflow, Python, and SQL.
• Model and maintain clean, well-structured datasets in Snowflake that support domains such as revenue attribution, financial planning, and campaign performance.
• Collaborate with stakeholders and analytics teams to define data requirements and provide clean, well-documented datasets.
• Develop and maintain data integration pipelines using Airbyte for ingesting from internal systems like PostgreSQL, and implement custom integrations for ingesting data from third-party external APIs.
• Build and support data infrastructure within AWS and/or GCP environments, leveraging cloud-native services such as S3, Lambda, Athena, and Iceberg for scalable storage and query execution.
• Own data pipeline observability, reliability, and operational excellence—including SLA tracking, logging, recovery strategies and data cataloging for discoverability and governance.
• Participate in planning, technical design reviews, code reviews, and incident response.
• Continuously improve data quality, observability, and development workflows across the team.

The skills and experience you bring to the job:

• Deep experience in data engineering, building and managing production-grade pipelines and models.
• Demonstrated proficiency in the development of robust and efficient ETL/ELT workflows, leveraging advanced Python and SQL development capabilities.
• Proficiency in Airflow, Snowflake, and Airbyte including strong knowledge of data integration and orchestration patterns.
• Demonstrated experience developing and maintaining cloud data pipelines on AWS or GCP, with proficiency in services like S3 and Lambda.
• Preferable experience with Athena, Apache Iceberg, or comparable query engines and table formats suited for large-scale, cloud-native analytics.
• Beneficial familiarity with event-driven or distributed frameworks such as Kafka or Spark.
• A system-oriented approach with a focus on scalability, observability, and long-term maintainability.
• Excellent collaboration and communication abilities, with comfort working across engineering, product, and analytics teams.