We're looking for a Senior Staff Software Engineer, Data to own the design and evolution of the data infrastructure that powers Albert OS. This role sits upstream of our AI and Product Engineering teams, so you define the data platform that they build on. It's 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 shape engineering direction across teams.
What you'll do
- Own the architecture and evolution of our core data platform, 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 and enforce data contracts, the interfaces that AI and Product Engineering teams consume, ensuring clean, structured, and trusted data surfaces across the stack.
- 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.
- Establish and enforce data security practices, including encryption boundaries, access controls, and tenant isolation.
- Build data platform capabilities that enable ML pipelines and Agent infrastructure, establishing the foundation for AI-native workloads at scale.
- 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.
What you bring
- 14+ 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.
- Experience defining data contracts and shared data interfaces consumed by AI/ML or product engineering teams.
- Knowledge of DynamoDB and PostgreSQL is a plus.
- Familiarity with Databricks Agent Bricks or agentic data pipeline patterns is a plus.
- Excellent communication skills and a track record of influencing cross-functional technical decisions.
Who you are
- Builder. You want to create foundational systems that others build on, not maintain what already exists.
- Curious. You care about how your work enables scientific discovery, not just engineering elegance.
- Rigorous. AI makes mistakes confidently. Our partners won't accept hand-waving, and neither will you.
- Collaborative. You partner effectively across Product Engineering, AI/ML, and Enterprise Engineering.
- Adaptable. You're comfortable in a fast-moving environment where priorities shift and you adjust without losing momentum.
Why Albert
- Revenue from day one. The product is loved and used by thousands of chemists.
- Small team. Flexible hours. Collaboration over process.
- No micro-management. You own your work.
- Building software for a sustainable future.
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 platform is the foundation everything else is built on.