Principal Data Platform Engineer

Principal Data Platform EngineerAbout the Role

We’re looking for a senior, hands-on engineer to build, operate, and evolve the foundations our entire data organization builds on — as our first dedicated data platform engineer, in a small, senior engineering organization with no separate DevOps or platform team.

This isn’t a role focused on building individual pipelines. Your job is to build the platform, frameworks, and tools that let our Data Engineers move fast while keeping the environment reliable, scalable, governed, and cost-efficient. The leverage here is real, not aspirational: company leadership and operations already run on our internal self-service BI platform with AI assistants and AI chat over governed data.

You’re a senior engineer who works independently, thinks in platforms and reusable frameworks, and still enjoys being deeply hands-on.

You’re comfortable moving between architecture and implementation, whether that means working with lakehouse technologies, optimizing analytical systems, designing cloud infrastructure, or writing production code.

You work AI-augmented by default, across engineering and operations. We don’t require that your current employer has adopted AI-native ways of working — we do expect that you’ve pushed them yourself regardless. You approach reliability, security, performance, and cost as engineering challenges to be solved through smart design and automation.

You may have led a team or engineering function before, but you deliberately want to stay close to the technology and continue building.

• Build and evolve our Databricks lakehouse environment, including Spark, Delta Lake, and Unity Catalog.
• Build and optimize our ClickHouse analytics layer for high-volume data.
• Develop reusable frameworks for ingestion, transformation, orchestration, and batch and streaming workloads.
• Build data quality capabilities, including automated checks, anomaly detection, freshness monitoring, and data contracts.
• Own the engineering behind our BI and self-service analytics platforms.
• Build the security and governance machinery: PII controls, access control, audit — enforced as code, not process.
• Build and maintain AWS infrastructure, CI/CD, observability, and deployment practices.
• Improve platform reliability and cost efficiency through automation, monitoring, and AI-driven incident investigation.
• Build the foundations that support ML and AI workloads alongside analytics.

A big part of the role is identifying problems engineers repeatedly solve themselves and turning them into platform capabilities.

• 8+ years in software, data, or platform engineering, with a track record of building platform components and frameworks that other engineers build on.
• Deep hands-on experience with Databricks or equivalent Spark + Delta Lake environments, including catalog-based governance (Unity Catalog or similar).
• Experience with ClickHouse or comparable high-performance OLAP databases (materialized views, aggregation strategies, performance tuning).
• Strong AWS background (S3, EC2, IAM, VPC; infrastructure-as-code).
• Excellent Python and SQL, with strong software engineering fundamentals: CI/CD, testing, code review culture.
• Solid understanding of data governance: PII handling, RBAC, least-privilege access, audit, and compliance (GDPR).
• Experience building observability for data systems: monitoring, alerting, SLAs, data quality automation.