Principal Software Engineer - Data Hub
New
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HubSpotAI-powered customer platform
Remote - USAFull-TimePrincipal
Salary313800 - 502080 USD per year
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Job Details
- Required Skills
- SparkDistributed Systems
Requirements
- Deep experience building large-scale data systems with Apache Spark and modern table formats like Apache Iceberg.
- Experience with efficient partitioning, clustering, and file layout for both heavy ingestion and low-latency reads.
- Applies distributed systems principles and CAP theorem pragmatically to design fault-tolerant, horizontally scalable services.
- Ability to balance availability, consistency, latency, and cost in distributed systems.
- Can turn ambiguous business goals into clear data models, contracts, and SLAs across multiple storage and compute layers (e.g., Iceberg, warehouses, logs, CRM stores).
- Experience with AI code agents and AI-assisted development tools.
- Strong habits around documentation, design reviews, testing, and observability.
- Experience establishing reliability and data quality standards.
Responsibilities
- Own core pieces of our data lake and analytics stack (e.g., Iceberg, Spark, batch and streaming pipelines) that power demand gen, segmentation, and scoring at scale.
- Design and evolve data systems that balance cost, latency, data freshness, and reliability, making explicit tradeoffs using concepts like CAP theorem, efficient partitioning, and storage layout.
- Partner closely with PM, product analytics, and GTM leaders to shape commercially meaningful solutions: better lead scoring, funnel visibility, audience building, and campaign attribution for marketers and sales.
- Help make Data Hub an AI-agent-forward platform, where curated, evergreen datasets automatically feed AI agents and reporting surfaces rather than requiring manual stitching or ad-hoc pipelines.
- Influence technical direction across the Data Hub product line and shape the architecture for unified profiles, segmentation, and datasets that other teams can build on.
- Write code and build systems while leading end-to-end delivery of high-impact, multi-quarter initiatives, setting standards for reliability, observability, testing, and incident response.
- Define reusable patterns for ingestion, transformation, quality, sync, and observability, mentor senior engineers and tech leads.
- Actively use AI-assisted development tools to speed iteration, reduce toil, and improve code quality, while defining best practices with the human-in-the-loop approach.
- Break down big, ambiguous problems into incremental milestones that deliver value early and often, balancing long-term platform bets with clear business impact.
- Model strong habits around documentation, design reviews, testing, and observability, and help establish reliability and data quality standards.
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