Staff Engineer, Data Platform

India·Bangalorelead

EngineeringDevops Engineer

1 views0 saves0 applied

Apply Now

Quick Summary

Overview

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,

Technical Tools

EngineeringDevops Engineer

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.

Responsibilities

~1 min read

→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.

Requirements

~1 min read

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.