Senior Software Engineer - Data Integration

What you'll do

• Design, build, and maintain reliable data pipelines for ingestion, transformation, and distribution of large-scale datasets, processing high volumes efficiently in production
• Develop ETL/ELT workflows using distributed computing frameworks on cloud infrastructure
• Design and build API-first services exposing ingestion, processing, and distribution capabilities to internal teams and external consumers
• Implement data quality validation, monitoring, and observability for owned components
• Contribute to platform-grade, reusable components enabling downstream teams and self-service consumption
• Take end-to-end ownership of key components within the data integration platform, driving reliability, scalability, and evolution
• Build new partner and destination integrations end-to-end, including throughput tuning and operational handoff
• Design and implement privacy-compliant data handling practices applying GDPR, CCPA, and company data governance policies
• Engage cross-functional stakeholders to ensure systems support all downstream use cases
• Drive technical design for components, produce design documents, contribute to architecture discussions, and align the team
• Conduct rigorous code reviews and uphold high standards for code quality, testability, and maintainability
• Mentor engineers through feedback, pairing, and design review
• Collaborate across adjacent teams, advocate for shared standards
• Own reliability of components end-to-end, monitor health, respond to incidents, and follow through on improvements
• Participate in on-call rotations and improve operational practices
• Drive improvements to CI/CD pipelines, deployment processes, and testing coverage

Requirements

• 8+ years of professional software engineering experience, with a strong focus on data engineering, backend systems, or distributed data infrastructure
• Proficient in Python and SQL; comfortable with JavaScript in full-stack or API contexts
• Strong hands-on experience with distributed processing frameworks (e.g., Spark, Databricks, or equivalent) working with large-scale datasets in production
• Practical experience across AWS, GCP, and Databricks, and their core data services
• Strong platform-thinking and API-first service design experience, building components that are reusable and consumable by downstream teams
• Hands-on experience with streaming and event-driven data processing frameworks (e.g., Kafka, Flink, Spark Streaming, or equivalent)
• Experience building or operating multi-tenant platforms with strong isolation and security boundaries
• Hands-on experience with workflow orchestration tools (Apache Airflow, dbt, Prefect, or equivalent)
• Experience incorporating AI and machine learning capabilities into production data workflows
• Solid understanding of data privacy regulations (GDPR, CCPA) and practical experience building compliant systems
• Clear communicator and cross-functional collaborator, able to articulate technical decisions and engage constructively in design reviews
• Active mentor, invests in others, gives direct feedback, and cares about raising the bar for the team
• Preferred: Strong familiarity with data warehousing and lakehouse technologies, with a preference for Snowflake
• Preferred: Exposure to ad tech, audience activation, data licensing, or digital media, familiarity with device graphs, audience segmentation, identity resolution, or measurement

Tech stack