Senior Staff Engineer- Data Platform

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 S 3, 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 .