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Senior Lead Software Engineer - Data Platform

Team Overview

The Data Platform organization owns the foundational data systems that power analytics, AI/ML, and product use cases across Klaviyo. This Senior Lead role is anchored in the Data Lake domain, while working broadly across Data Platform to support and align with adjacent teams.

In practice, this role helps shape and connect the technical direction across lakehouse storage and compute, data onboarding and orchestration, modeled data layers, and the operational automation that keeps the platform reliable, observable, and usable at scale. The role requires strong judgment in connecting work across teams rather than optimizing only within a single technical boundary.

The scope is not just to improve one system in isolation, but to make the broader data platform more trustworthy, scalable, and easier to operate. That includes aligning roadmaps across teams, supporting high-impact and complex cross-team projects, and helping represent Data Platform clearly to partner teams and stakeholders.

How You’ll Make an Impact

As a Senior Lead Software Engineer in Data Platform, with primary focus on Data Lake, you will:

• Independently own and drive high-impact technical objectives that span multiple Data Platform teams, especially Data Lake while supporting work that intersects with Data Automation and Data Warehouse.


• Help align technical roadmaps across Data Platform so teams are making coherent investments against shared priorities, dependencies, and long-term platform direction.


• Lead large, complex, cross-team initiatives from discovery through rollout and long-term ownership, especially where success depends on coordination across organizational and technical boundaries.


• Make high-judgment architectural decisions across core platform systems including lakehouse storage and compute, orchestration, modeled data layers, and operational automation, and create reference patterns that other teams can reuse.


• Establish paved paths, standards, and shared abstractions that reduce repetitive manual work and make onboarding, operating, and evolving data systems dramatically faster and more reliable across the platform.


• Partner closely with engineering, product, analytics, AI/ML, and governance stakeholders, and represent Data Platform clearly in cross-functional discussions that require both technical depth and strong external communication.


• Help drive alignment on complex projects by clarifying trade-offs, surfacing dependencies early, and ensuring teams stay coordinated as priorities or execution details shift.


• Be accountable, with team and engineering leaders, for the long-term technical health of the systems in your scope across reliability, scalability, performance, cost, security, and operational excellence.


• Own the response to complex platform issues when needed, working directly with engineers across teams during high-severity situations and turning lessons learned into lasting improvements in architecture, operations, and communication.


• Invest in the growth of senior and lead engineers through design reviews, RFC feedback, architectural coaching, and active participation in senior engineering hiring.

Who You Are

• You are passionate about building platforms for the long term and can balance technical quality, engineering velocity, and business impact across multiple teams.


• You have 12+ years of software engineering experience and deep knowledge of distributed systems, data platform architecture, and large-scale analytical processing.


• You bring deep expertise in one or more core Data Platform problem spaces such as lakehouse architecture, data ingestion and transformation, distributed compute, data warehousing, platform reliability, or developer-facing data infrastructure, while maintaining strong system-level thinking across interconnected areas.


• You have independently led large, ambiguous, multi-quarter programs and have a track record of making clear architectural calls, breaking through technical obstacles, and leading teams through critical operational moments.


• You have strong hands-on experience with the kinds of systems this role touches, including Iceberg-based storage, Spark or EMR-based compute, Airflow-based orchestration, Python-based data platform tooling, modeled data systems, and AWS-hosted infrastructure.


• You understand how to build and operate trustworthy data platforms, including schema evolution, materialization patterns, observability, backfills, repairs, audits, access controls, and cost-aware platform operations.


• You are highly effective at driving outcomes through influence and technical leadership rather than direct authority, and you communicate clearly with both technical and non-technical partners.


• You are passionate about mentoring other engineers and creating the patterns, guidance, and opportunities that help senior engineers and leads grow.


• You enjoy improving how systems and teams work, whether through better architecture, tooling, workflows, or operating practices.

Nice to Have

• Experience with custom built databases and CDM-style modeled data layers, especially transforming landing data into durable, queryable business entities.


• Experience building or extending managed table frameworks such as Materialized Views and related operational tooling.


• Experience with data governance, ownership, discoverability, residency, or regulated-data programs such as GDPR or HIPAA.


• Experience building platform automation for backfills, repairs, optimization, autoscaling, and agentic support for engineers operating complex data systems.

Technologies We Use

Core technologies and platform components used by the Data Lake team include:

• Iceberg S3 tables for the foundational storage layer.


• Spark / EMR for large-scale analytical compute.


• Airflow for orchestration, scheduling, and workflow tracking.


• Materialized Views and Python-based platform frameworks for managed Iceberg tables and related data tooling.


• Landing-table loaders and CDM tables for ingestion and modeled data transformations that support KDB.


• Audit and operational automation for maintenance, observability, backfills, repairs, optimization, autoscaling, and access controls.


• AWS-hosted infrastructure supporting the lakehouse platform and its surrounding systems.