Principal Engineer, Data Infrastructure

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Own the technical direction and delivery for Klaviyos data platform - streaming batch compute storage/lakehouse and governance - now the foundation for autonomous agent-driven experiences. Klaviyos data platform processes billions of events daily across billions of consumer profiles for hundreds of thousands of brands and increasingly that data is consumed not just by people but by a growing number of AI agents acting on customers behalf. The footprint grows on two axes at once everlarger data volumes and a steady stream of new data scenarios (new sources domains and consumption patterns) and your systems have to scale on both.

Youll design and ship systems that are fast reliable costefficient and safe for autonomous access creating paved roads for data producers and consumers human and agent alike. This is an individualcontributor role (no direct reports); you lead through architecture code and influence. Expectations align to Lead/Principal IC behaviors: establishing SLOs driving technical evolution and acting as the interface across teams.

What Youll Do

• Design and implement core data platform capabilities (e.g. event ingestion/CDC stream processing batch orchestration data lake/warehouse patterns catalog/lineage governance access and compliance).
• Define and uphold SLOs for data freshness availability and correctness; author/run readiness reviews incident response and postincident learning for your domain.
• Author ADRs/RFCs land data contracts and schema governance and standardize connectors and templates that accelerate developer velocity.
• Profile tune and rightsize systems for performance and cost; partner with FinOps on uniteconomics guardrails.
• Pair with product teams and analytics/ML to expose the right abstractions and unblock customer value quickly.
• Contribute highquality code and reviews; mentor Staff/ Sr. engineers across pillars through example and enablement (not line management).
• Use AI to streamline data workflows from authoring and testing pipelines to catalog/search and DQ so analysts ML and product teams move faster with confidence.

Who You Are

• Experience: 10 years building and operating distributed data systems (e.g. Kafka/PubSub Flink/Spark/Beam Airflow/Dagster Iceberg/Delta/Hudi; Snowflake/BigQuery; object storage) with multitenant reliability.
• Technical expertise: Data ingestion/CDC stream processing batch orchestration lakehouse patterns catalog/lineage governance and access controls measured by freshness availability and correctness SLOs.
• AI tools & automation: You apply ML/GenAI to data platforms - semantic catalog search autodocs dataquality anomaly detection SQL/pipeline generation - with humanintheloop review and privacy controls.
• Influence & enablement: You land data contracts connectors and templates that speed delivery for producers and consumers; you mentor via design docs and pairing.
• AI fluency (Klaviyo default): You experiment learn fast and share AI wins responsibly.

Nice to Haves

• Regional isolation/replication strategies privacybydesign and data governance in regulated contexts.
• Adopted paved roads: Producers/consumers are on standard ingestion processing and storage paths; schema governance and contracts reduce breakage.
• SLOs & efficiency: 99.9% freshness for key domains; measurable cost/TB reductions; production debugging is faster with defined readiness reviews and incident learning.
• AIaugmented data operations: Semantic discovery and autodocumentation cover the majority of highvalue datasets; AIassisted DQ monitors reduce data incidents on top pipelines by 2540%; pipeline authoring and review times drop 1525% with AI in the loop.

Success in 6 - 12 Months

• Adopted paved roads for producers/consumers; 99.9% freshness SLO for key domains; measurable cost/TB reductions.
• One or more stepchange improvements (e.g. 2 faster ingest analytics latency) demonstrated with metrics and postincident trenddown.