Principal Engineer – Data Platform

Role Overview

As a Principal Engineer – Data Platform, you will drive the next wave of architectural direction and foundational data capabilities that power Safe’s multi-tenant data platform, analytics, and intelligence systems.

You will partner with engineering leadership, product, and cross-functional teams to define, build, and evolve the core data systems that allow Safe to scale securely and reliably.

You’ll not just execute — you’ll lead and mentor, influence technical direction across the org, and champion best practices in data architecture, lakehouse design, scalability, reliability, and observability.

Key Responsibilities

• Architect & Lead Data Platform Strategy

Drive the long-term vision for Safe’s data platform: lakehouse architecture, open table formats (Apache Iceberg), data ingestion frameworks, streaming pipelines, and data serving layers.

Evaluate alternative architectures, lead design reviews, and ensure consistency across solutions.

• Operational Excellence & Scalability

• Ensure data systems operate at high performance with strong guarantees on data freshness, accuracy, and availability.
• Lead efforts in performance tuning, large-scale data handling (billions of records), cost efficiency, and capacity planning.

• Cross-cutting “Horizontal” Ownership

• Lead horizontal capabilities such as data ingestion, data modeling, streaming pipelines, data quality, lineage, and data observability.
• Drive self-serve data platform capabilities for internal teams.

• Drive Engineering Standards & Best Practices

• Establish best practices for data modeling, schema evolution, partitioning, compaction, and pipeline design.
• Ensure strong data quality, testing, and reliability standards across the platform.
• Mentor senior and staff engineers and elevate overall technical rigor in data systems.

• Collaboration & Influence

• Work closely with Product, AI, Security, and Platform leadership to align data architecture with business goals.
• Clearly articulate trade-offs, constraints, and design decisions.

• End-to-End Ownership

• From ingestion to transformation to serving — own critical data flows end-to-end and ensure production-grade reliability.
• Guide teams through complex data challenges and maintain robustness in production systems.

Must-Have Qualifications

• Experience: 10+ years in software/data engineering, including 4+ years as a senior/lead/principal engineer in data platform, backend, or infrastructure systems.
• Lakehouse & Iceberg Expertise:

• Deep hands-on experience with Apache Iceberg (mandatory) and modern lakehouse architectures.
• Strong understanding of partitioning strategies, schema evolution, compaction, snapshotting, and large-scale table optimization.

• Distributed Data Systems:

Proven track record designing and building large-scale data pipelines, including batch and streaming systems, event-driven architectures, and data ingestion frameworks.

• Strong Language Skills:

Expert proficiency with Python, Go, or TypeScript (or equivalent); familiarity with multiple languages is a plus.

• Storage & Messaging:

Deep experience with data lakes (S3), and systems like Kafka, Spark, Flink, or equivalent processing frameworks.

• Cloud & Infra:

Hands-on experience with AWS (or equivalent), containerization (Docker), orchestration (ECS/Kubernetes), and IaC (Terraform/CloudFormation).

• Observability & Reliability:

Expertise in data observability, pipeline monitoring, data quality systems, SLAs, and failure recovery mechanisms.

• Security & Multi-Tenancy:

Strong understanding of data isolation, governance, access control, and secure data design in multi-tenant systems.

• Leadership & Communication:

Excellent written and verbal communication. Comfortable influencing cross-functional stakeholders across geographies.

• Problem-Solving & Judgement:

Strong fundamentals in system design, tradeoff analysis, and building scalable data systems.

Preferred / Nice-to-Have

• Experience building B2B SaaS data platforms at scale
• Exposure to AI/ML pipelines, feature stores, or vector databases
• Experience with real-time analytics and streaming systems
• Experience in developer-facing data platforms (self-serve data, internal tooling)
• Exposure to Snowflake or similar analytical warehouses
• Experience in regulated or security-sensitive environments (ISO 27001, SOC2)