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About the role
The Data Platform organization at Klaviyo owns the foundational data infrastructure that powers analytics, AI/ML models, and customer-facing product experiences across the entire business.
As a Senior Lead Software Engineer in the Data Platform group (Data Lake Domain), you will shape and align the overarching technical direction across our lakehouse storage and compute engines, data onboarding and orchestration pipelines, modeled data layers, and operational automation systems. This is a principal-level engineering leadership role for a hands-on systems architect who can lead complex, multi-quarter initiatives spanning Apache Iceberg, Spark/EMR compute, Airflow orchestration, and AWS cloud infrastructure—ensuring the data platform remains trustworthy, performant, and cost-aware at scale.
Key Responsibilities
- Multi-Team Architectural Leadership: Drive high-impact technical objectives across the Data Lake, Data Automation, and Data Warehouse teams to build a coherent, unified data platform.
- Lakehouse System Architecture: Make high-judgment architectural calls across Apache Iceberg S3 storage tables, Spark/EMR compute clusters, Airflow DAG workflows, and Python-based platform abstractions.
- Paved Paths & Developer Experience: Establish reusable frameworks, standards, and self-serve abstractions that simplify data onboarding, backfills, schema evolution, and materialization.
- Cross-Team Alignment & Execution: Lead multi-quarter, cross-functional programs from discovery through rollout; clarify technical trade-offs, identify dependencies early, and maintain coordination across product and AI/ML teams.
- Operational Excellence & Reliability: Accountable for long-term platform health covering reliability, latency, data freshness, auditability, access controls, cost optimization, and high-severity incident response.
- Engineering Mentorship & Hiring: Coach senior and lead engineers through design reviews, RFC evaluations, and architectural mentorship; actively participate in senior engineering hiring.
Requirements
Large-Scale Distributed Systems & Data Depth
- 12+ years of professional software engineering experience specializing in distributed systems, data platform architecture, and large-scale analytical processing.
- Proven expertise across core data platform problem spaces: lakehouse storage, distributed compute, data warehousing, ingestion/transformation pipelines, and developer-facing data infrastructure.
- Track record of independently leading large, ambiguous, multi-quarter technical programs across organizational boundaries.
- Hands-On Core Stack Experience:
- Storage & Compute: Apache Iceberg, AWS S3, Apache Spark, AWS EMR.
- Orchestration & Tooling: Apache Airflow, Python-based data platform frameworks, custom ORM/data loaders.
- Cloud Infrastructure: AWS cloud architecture, cost-aware resource allocation, and automated capacity scaling.
- Deep understanding of data quality and reliability mechanics: schema evolution, materialization, backfills, data repairs, lineage, audits, and access control models.
Preferred Qualifications
- Experience with custom-built databases and Common Data Model (CDM) style layers transforming raw landing data into durable business entities.
- Background in building or extending managed table frameworks (e.g., Materialized Views) and operational management tooling.
- Practical experience with data governance, discoverability, data residency, or compliance frameworks (GDPR, HIPAA).
- Track record of building platform automation for backfills, repairs, auto-scaling, or agentic support tooling for operating complex data platforms.
Total Rewards & Benefits
- Compensation & Equity: Base salary of $216,000 – $324,000 USD + participation in annual cash bonus plan + competitive equity grants.
- Comprehensive Healthcare: Robust medical, dental, and vision insurance options for employees and eligible dependents.
- Financial Security & Retirement: 401(k) retirement savings plan, life insurance, and disability coverage.
- Workplace Culture & Perks: Professional development stipends, flexible time off, and access to modern AI productivity tooling.

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