Design and build reliable data pipelines for ingestion, transformation, and distribution of large-scale datasets, making sound architectural decisions within your scope.
Develop ETL/ELT workflows using distributed computing frameworks on cloud infrastructure, applying engineering judgment to design and implementation decisions within your scope, partnering with senior engineers on broader architecture.
Build API-first services that expose ingestion, processing, and distribution capabilities to internal teams and external consumers, with attention to reliability, clear contracts, and ease of integration.
Implement data quality validation, monitoring, and observability for the components you own, ensuring reliability and correctness in production.
Build reusable platform components with a clear understanding of how they serve downstream consumers.
Take ownership of components within the data integration platform, ingestion, processing, or distribution, and drive their reliability and iteration.
Build partner and destination integrations end-to-end, including throughput tuning and operational handoff.
Apply GDPR, CCPA, and Samba data governance requirements to the systems you build.
Collaborate with immediate team members and engage with adjacent teams to understand downstream use cases.
Drive technical design for components within your scope, producing design documents and participating actively in architecture discussions.
Conduct code reviews and uphold strong standards for code quality, testability, and maintainability across the team.
Build working relationships with adjacent teams and reason about cross-functional requirements.
Own the reliability of your components, monitor their health, respond to incidents, and follow through on post-mortem improvements.
Participate in on-call rotations and contribute to improving operational practices across the team.
Build and maintain CI/CD pipelines, deployment processes, and testing coverage for team systems.
5+ years of professional software engineering experience with a Bachelor's degree in Computer Science, Software Engineering, or a related technical field (or 3+ years with a Master's, a PhD with no prior experience, or equivalent), with a meaningful focus on data engineering, backend systems, or distributed data infrastructure.
Proficiency in Python and SQL; ability to write clean, well-tested, production-ready code.
Hands-on experience with distributed processing frameworks (e.g., Spark, Databricks, or equivalent) in production.
Hands-on production experience building cloud-native data systems on AWS, GCP, or Databricks, including their core data services.
Experience building API-first services with a focus on correctness and reliability.
Working experience with streaming or event-driven data processing frameworks (e.g., Kafka, Flink, Spark Streaming, or equivalent).
Experience with workflow orchestration tools (Apache Airflow, dbt, Prefect, or equivalent).
Familiarity with data privacy regulations (GDPR, CCPA) and an understanding of how they affect system design.
A clear communicator who participates actively in design discussions, shares context proactively, and works well across a team. Comfortable advising more junior engineers on technical matters within your area.
Familiarity with data warehousing and lakehouse technologies, with a preference for Snowflake.
Experience building or operating multi-tenant data platforms.
Experience with AI/ML integration in production data workflows.
Exposure to ad tech, audience activation, data licensing, or digital media — familiarity with concepts such as device graphs, audience segmentation, identity resolution, or measurement.