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, applying strong engineering judgment to architectural and implementation decisions within your scope.
Design and build API-first services that expose ingestion, processing, and distribution capabilities to internal teams and external consumers, with a focus on reliability, clear contracts, and ease of integration.
Implement data quality validation, monitoring, and observability for the components you own, ensuring reliability and correctness at scale.
Contribute to platform-grade, reusable components that enable downstream teams and support self-service consumption.
Take end-to-end ownership of key components within the data integration platform, ingestion, processing, or distribution, and drive their reliability, scalability, and evolution.
Build new partner and destination integrations end-to-end, including throughput tuning, operational handoff, and contract-driven delivery.
Design and implement privacy-compliant data handling practices, applying GDPR, CCPA, and Samba's data governance policies, including support for clean room and privacy-preserving data collaboration workflows.
Engage cross-functional stakeholders, product, data science, and partner teams, to ensure the systems you build support all downstream use cases.
Drive technical design for components within your scope, producing clear design documents, actively contributing to architecture discussions, and aligning the team on well-reasoned solutions.
Conduct rigorous code reviews and uphold high standards for code quality, testability, and maintainability.
Mentor engineers on the team through structured feedback, pairing, and design review.
Collaborate across adjacent teams, understanding their constraints and requirements, and advocating for shared standards where applicable.
Own the reliability of your components end-to-end, monitor their health, respond to incidents, and follow through on post-mortem improvements with rigor.
Participate in on-call rotations and contribute actively to improving operational practices across the team.
Drive improvements to CI/CD pipelines, deployment processes, and testing coverage for your team's systems.
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.
A clear communicator and cross-functional collaborator, able to articulate technical decisions, engage constructively in design reviews, and navigate complex stakeholder relationships outside your immediate team.
An active mentor, you invest in others, give direct feedback, and care about raising the bar for the team as a whole.
Strong familiarity with data warehousing and lakehouse technologies, with a preference for Snowflake.
Exposure to ad tech, audience activation, data licensing, or digital media, familiarity with concepts such as device graphs, audience segmentation, identity resolution, or measurement.