Staff Engineer, Data Platform

Albert's mission is to help chemists change the world. We build Albert OS, the first AI-native operating system for materials science, helping enterprise R&D teams capture their scientific knowledge, run smarter experiments, and bring better materials to market faster. Our data systems are at the heart of that mission

We're looking for a Staff Software Engineer, Data to lead the design and evolution of the data infrastructure that powers Albert OS. This is a high-impact role for someone who thinks deeply about data architecture across analytical and transactional systems, cares about correctness and reliability at scale, and can influence engineering direction across teams.

• Own the architecture and evolution of our core data platforms, spanning Lakehouse infrastructure, analytical pipelines, and transactional systems.
• Design and build scalable data ingestion and transformation pipelines (batch and streaming) with a strong emphasis on data quality, reliability, and downstream usability.
• Define data modeling standards across our stack, including dimensional, medallion, and schema-first patterns.
• Drive performance optimization across SQL and NoSQL systems, with a focus on query efficiency, partitioning strategies, and throughput at scale.
• Collaborate closely with API and Data Science teams to build robust pipelines and shared data contracts.
• Establish and enforce data security practices, including encryption boundaries, access controls, and tenant isolation.
• Set standards for observability across data systems using tools like Grafana, CloudWatch, and OpenTelemetry.
• Mentor engineers across the org and lead architectural decisions through review and documentation.

• 8+ years of experience in data engineering or a closely related discipline, with a focus on large-scale distributed systems.
• Hands-on experience with Databricks and lakehouse architecture patterns.
• Strong understanding of OLAP systems and experience designing star schema and galaxy schema data models.
• Strong understanding of transactional systems design, including consistency models, write patterns, and operational reliability.
• Expertise in data modeling across both SQL and NoSQL ecosystems.
• Strong programming skills in Python and Spark with an emphasis on clean, maintainable code.
• Hands-on experience with AWS services including S3, Lambda, and IAM.
• Familiarity with observability stacks (Grafana, CloudWatch) and experience debugging production bottlenecks.
• Knowledge of DynamoDB and PostgreSQL is a plus.
• Excellent communication skills and a track record of influencing cross-functional technical decisions.

• Builder Mindset: Wants to create foundational systems that others build on.
• Curiosity: Genuine interest in how your work enables scientific discovery.
• Rigor: Commitment to quality. AI makes mistakes confidently, and our customers won't accept hand-waving.
• Collaboration: Partners effectively with engineers across Product Engineering, AI/ML, and Enterprise Engineering.
• Adaptability: Comfortable in a dynamic, fast-moving environment where priorities shift.

• Revenue from day one. The product is loved and used by thousands of users.
• Small team. Flexible hours. Collaboration over process.
• No micro-management. You'll own your work.
• Building software for a sustainable future.